IL295179A - Methods of diagnosing and classifying viral infections - Google Patents
Methods of diagnosing and classifying viral infectionsInfo
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- IL295179A IL295179A IL295179A IL29517922A IL295179A IL 295179 A IL295179 A IL 295179A IL 295179 A IL295179 A IL 295179A IL 29517922 A IL29517922 A IL 29517922A IL 295179 A IL295179 A IL 295179A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
- G01N33/56983—Viruses
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/005—Assays involving biological materials from specific organisms or of a specific nature from viruses
- G01N2333/08—RNA viruses
- G01N2333/165—Coronaviridae, e.g. avian infectious bronchitis virus
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/52—Assays involving cytokines
- G01N2333/521—Chemokines
- G01N2333/522—Alpha-chemokines, e.g. NAP-2, ENA-78, GRO-alpha/MGSA/NAP-3, GRO-beta/MIP-2alpha, GRO-gamma/MIP-2beta, IP-10, GCP-2, MIG, PBSF, PF-4 or KC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
- G01N2333/70575—NGF/TNF-superfamily, e.g. CD70, CD95L, CD153 or CD154
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/50—Determining the risk of developing a disease
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/56—Staging of a disease; Further complications associated with the disease
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- Tropical Medicine & Parasitology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Description
METHODS OF DIAGNOSING AND CLASSIFYING VIRAL INFECTIONS
RELATED APPLICATIONS
This application claims the benefit of priority of US Patent Application No. 62/967,409
filed 29 January, 2020, US Patent Application No. 62/975,802 filed 13 February, 2020 and US
Patent Application No. 63/019,451 filed 4 May, 2020, the contents of which are incorporated herein
by reference in their entirety. This application further claims the benefit of priority of US Patent
Application No. 63/006,758 filed 8 April, 2020, 63/014,214 filed 23 April, 2020, 63/030,937 filed
28 May, 2020 and 63/085,189 filed 30 September 2020.
SEQUENCE LISTING STATEMENT
The ASCII file, entitled 86297 Sequence Listing.txt, created on 29 January 2021,
comprising 57,685 bytes, submitted concurrently with the filing of this application is incorporated
herein by reference.
FIELD AND BACKGROUND OF THE INVENTION
The present invention, in some embodiments thereof, relates to methods of diagnosing and
classifying the severity of viral infections and more specifically to coronaviral infections.
Disease assessment is one of the most important tasks in management of patients. Predicting
patient prognosis may affect various aspects of patient management including treatment, diagnostic
tests (e.g., microbiology, blood chemistry, radiology etc.), and admission. Timely identification of
patients with higher chance for poor prognosis may result in more aggressive patient management
procedures including for example, intensive care unit (ICU) admission, advanced therapeutics,
invasive diagnostics or surgical intervention, which could reduce complications and mortality.
WO 2013/117746 teaches biomarkers including TNF-related apoptosis-inducing ligand
(TRAIL) for distinguishing between a bacterial and viral infection.
Additional background art includes WO 2016/024278, WO2018/060998 and
WO2018/060999.
SUMMARY OF THE INVENTION
According to an aspect of the present invention, there is provided a method of classifying
the severity of a coronaviral infection of a subject, comprising measuring the TRAIL and/or IP10
protein level in a body liquid sample of the subject, wherein the level is indicative of the severity
of the coronaviral infection.
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According to embodiments of the invention the subject does not present with symptoms of
an infectious disease.
According to embodiments of the invention the subject presents with symptoms of an
infectious disease.
According to embodiments of the invention the symptoms comprise fever.
According to embodiments of the invention the method further comprises measuring the
level of at least one determinant set forth in Table 1.
According to embodiments of the invention the at least one determinant is selected from the
group consisting of PCT, IL-6 and CRP.
According to embodiments of the invention the method further comprises measuring the
level of at least one determinant selected from the group consisting of ferritin, d-dimer and lactate.
According to embodiments of the invention the method further comprises measuring the
level of CRP.
According to embodiments of the invention the method further comprises treating the
subject who has been classified as severe with a management selected from the group consisting
of oxygen therapy, non-invasive ventilation, mechanical ventilation, invasive monitoring, last-
resort drug, sedation, intensive care admission, surgical intervention, hospital admittance, anti-
viral drug, anti-viral regimen, anti-bacterial drug, anti-fungal drug, immune-globulin treatment
isolation, glucocorticoid therapy, extracorporeal membrane oxygenation and kidney replacement
therapy.
According to embodiments of the invention the coronavirus of the coronaviral infection is
severe acute respiratory syndrome coronavirus (SARS-CoV-2) and Middle East respiratory
syndrome coronavirus (MERS-CoV).
According to an aspect of the present invention, there is provided a method of managing
treatment of a disease which brings about a dysregulation of an immune and/or inflammatory
response of a subject, the subject already being medicated with a first treatment to treat the disease
comprising:
(a) measuring an amount of IP10 and/or TRAIL in a sample of the subject; and
(b) altering the treatment or treatment course when the level of IP10 is above a
predetermined threshold and/or when the level of TRAIL is below a predetermined threshold in
the sample.
According to embodiments of the invention the disease is a viral disease.
According to embodiments of the invention the viral disease is a respiratory viral disease.
According to embodiments of the invention the respiratory viral disease is COVID-19.
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According to embodiments of the invention the respiratory viral disease comprises acute
respiratory distress syndrome (ARDS).
According to embodiments of the invention the virus of the respiratory viral disease is
selected from the group consisting of RSV, Flu A, Flu B, HCoV and SARS-Cov-2.
According to embodiments of the invention the predetermined threshold for IP10 is above
1000 pg/ml.
According to embodiments of the invention the method further comprises determining the
length of time for which the level of IP10 is above the predetermined threshold, wherein the length
of time is indicative that the treatment or treatment course should be altered.
According to embodiments of the invention the predetermined threshold for TRAIL is
below 25 pg/ml.
According to embodiments of the invention the method further comprises determining the
length of time for which the level of TRAIL is below a predetermined threshold, wherein the length
of time is indicative that the treatment or treatment course should be altered.
According to embodiments of the invention the subject is hospitalized.
According to embodiments of the invention the subject is non-hospitalized.
According to embodiments of the invention the subject is in intensive care.
According to embodiments of the invention the method further comprises re-measuring the
amount of IP10 and/or TRAIL following the altering.
According to embodiments of the invention the re-measuring is effected no more than 12
hours following the altering the treatment.
According to embodiments of the invention, steps (a) and (b) are repeated at least three
times during the course of the disease.
According to embodiments of the invention the first treatment comprises administering a
therapeutic agent.
According to embodiments of the invention the first therapeutic agent comprises a steroid
agent.
According to embodiments of the invention the therapeutic agent comprises an agent which
reduces the activity or amount of IL-6.
According to embodiments of the invention the altering the treatment comprises altering
the dose of the therapeutic agent based on the level of IP10 and/or TRAIL.
According to embodiments of the invention step (b) is effected no more than 12 hours
following step (a).
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According to embodiments of the invention the altering the treatment comprises
terminating the first treatment.
According to embodiments of the invention the altering the treatment comprises initiating
a second treatment.
According to embodiments of the invention the altering the treatment further comprises
initiating a second treatment.
According to an aspect of the present invention, there is provided a method of predicting
onset of a cytokine storm in a subject having a disease which brings about a dysregulation of an
immune and/or inflammatory response, the method comprising:
(a) measuring an amount of IP10 in a sample of the subject; and
(b) predicting a cytokine storm based on the level of IP10.
According to an aspect of the present invention, there is provided a method of distinguishing
between a system shutdown diagnosis and a cytokine storm diagnosis in a subject with a viral
disease, comprising measuring the expression level of TRAIL in a sample of the subject, wherein
when the level of TRAIL is above a predetermined level, a cytokine storm diagnosis is ruled in
and/or when the level of TRAIL is below a predetermined level, a system shutdown diagnosis is
ruled in.
According to embodiments of the invention the method further comprises analyzing the
level of at least one additional determinant set forth in Tables 1 or 2.
According to embodiments of the invention the amount of IP10 above 1000 pg/ml is
indicative of onset of cytokine storm.
According to embodiments of the invention the predicting comprises an estimation of the
time of onset.
According to embodiments of the invention the cytokine storm is the result of an immune-
based therapy selected from the group consisting of Tumor-Infiltrating Lymphocyte (TIL)
Therapy, Engineered T Cell Receptor (TCR) Therapy, Chimeric Antigen Receptor (CAR) T Cell
Therapy and Natural Killer (NK) Cell Therapy.
According to embodiments of the invention the disease is selected from the group
consisting of infectious disease, Ulcerative colitis, Crohn’s disease, Rheumatoid arthritis, Sjogren
syndrome, Kawasaki disease, Guillain-Barre syndrome, Lupus, Asthma, COPD, Multiple
sclerosis, vasculitis and Psoriasis.
According to embodiments of the invention the infectious disease is a viral disease.
According to an aspect of the present invention, there is provided a method of diagnosing
the severity of a viral disease of a subject, comprising measuring the expression level of a first
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determinant set forth in Table 1 and the level of a clinical measurement set forth in Table 2 in a
sample of the subject, wherein the levels are indicative of the severity of the viral disease.
According to embodiments of the invention the virus of the viral infection is selected from
the group consisting of Adenovirus, Bocavirus, Coronavirus, Enterovirus, Influenza virus,
Metapneumovirus, Parainfluenza virus, Respiratory syncytial virus and Rhinovirus.
According to embodiments of the invention the viral disease is a respiratory viral disease.
According to embodiments of the invention the viral disease is transmitted via an influenza
virus.
According to embodiments of the invention the viral disease is transmitted via a
coronavirus.
According to embodiments of the invention the coronavirus is selected from the group
consisting of severe acute respiratory syndrome coronavirus (SARS-CoV-2) and Middle East
respiratory syndrome coronavirus (MERS-CoV).
According to embodiments of the invention the method further comprises measuring the
amount of at least one additional determinant selected from the group consisting of IL-6, PCT and
CRP.
According to an aspect of the present invention, there is provided a method of analyzing the
contagiousness of a non-symptomatic subject suffering from an infection, comprising measuring
the level of a determinant selected from the group consisting of TRAIL and IP10 in a body liquid
sample of the subject, wherein:
(i) when the level of TRAIL is higher than the level of TRAIL in a subject who does not
have an infection, it is indicative that the non-symptomatic subject is contagious; and/or
(ii) when the level of IP10 is higher than the level of IP-10 in a subject who does not have
an infection, it is indicative that the non-symptomatic subject suffering from an infection is
contagious.
According to an aspect of the present invention, there is provided a method of analyzing the
degree of contagiousness of a test subject suffering from an infection comprising measuring the
level of a determinant selected from the group consisting of TRAIL and IP10 in a body liquid
sample of the subject, wherein the level of TRAIL and/or IP10 is proportional to the degree of
contagiousness of the test subject suffering from the infection.
According to embodiments of the invention the test subject is non-symptomatic.
According to embodiments of the invention the test subject is symptomatic.
According to embodiments of the invention the non-symptomatic subject has been exposed
or is suspected of being exposed to a subject known to have a viral infection.
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According to embodiments of the invention the non-symptomatic subject is recovering from
a viral infection.
According to embodiments of the invention the method further comprises recommending
isolation of the subject upon diagnosis as being contagious.
According to embodiments of the invention the method further comprises treating the non-
symptomatic subject who has been diagnosed as being contagious with a treatment selected from
the group consisting of an anti-viral agent, mechanical ventilation, invasive monitoring, last-resort
drug, sedation, intensive care admission, surgical intervention, hospital admittance and isolation.
According to embodiments of the invention:
(i) when the level of TRAIL is at least 10 % higher than the level of TRAIL in a subject
who does not have an infection, it is indicative that the non-symptomatic subject is contagious;
and/or
(ii) when the level of IP10 is at least 25 % higher than the level of TRAIL in a subject who
does not have an infection, it is indicative that the non-symptomatic subject is contagious.
According to an aspect of the present invention, there is provided a method of analyzing
biological data, the biological data containing expression values of the TRAIL protein, the IP10
protein, and the CRP protein in a body liquid sample of the subject, the method comprising:
by a hardware processor, receiving the expression values, calculating a distance between a
segment of a curved line and an axis defined by a direction, determining a score classifying a
severity of a coronaviral infection of the subject based on the distance, and generating an output
indicative of the score;
wherein the distance is calculated at a point over the curved line defined by a coordinate
along the direction, the coordinate being defined based on the expression values,
wherein at least 90% of the segment is between a lower bound line f( )- 0 and an upper
bound line f( )+ , wherein each of the and the is less than 0.5, and wherein the f( ) comprises
1 0 1
a multiplication between a saturation function of the coordinate, and a saturation function of the
expression value of the CRP protein.
According to embodiments of the invention the saturation function of the coordinate is
linearly proportional to 1/(1+Exp(- )).
According to embodiments of the invention the saturation function of the expression value
-h
of the CRP protein is linearly proportional to 1/(1+(CRP/CRP ) ), where CRP and h are
0 0
predetermined shift and width parameters.
According to embodiments of the invention the predetermined shift parameter CRP is from
0
about 1 mg/L to about 1000 mg/L.
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According to embodiments of the invention the predetermined width parameter h is from
about 2 to about 100.
According to embodiments of the invention the f( ) equals p(1-w)+w, the p being the
saturation function of the coordinate, and the w being the saturation function of the expression
value of the CRP protein.
According to embodiments of the invention the coordinate is defined at least based on a
linear combination of the expression values.
According to embodiments of the invention the method comprises comparing each of the
expression values to at least one respective threshold, wherein the coordinate is defined at least
based on a result of the comparison.
According to embodiments of the invention the offset term of the linear combination is
from about 0.9 to about 0.9 , the being defined as log(p /(1-p )) - log(p /(1-p )), the
max max min min
p being more than 0.9 and less than 1, and the p being from about 0.01 to about 0.5.
max min
According to embodiments of the invention the coefficient of the expression value of the
TRAIL protein in the linear combination is from about -0.04/TRAIL ml/pg to
min
about -0.04 /TRAILmin ml/pg, the being defined as log(pmax/(1-pmax)) - log(pmin/(1-pmin)), the
pmax being more than 0.9 and less than 1, the pmin being from about 0.01 to about 0.5, and the
TRAIL being from about 0 to about 20 pg/ml.
min
According to embodiments of the invention the coefficient of the expression value of the
IP-10 protein in the linear combination is from about 0.0006/IP10min ml/pg to about
0.0006 /IP10min ml/pg, the being defined as log(pmax/(1-pmax)) - log(pmin/(1-pmin)), the pmax being
more than 0.9 and less than 1, the pmin being from about 0.01 to about 0.5, and the IP10min being
from about 0 to about 150 pg/ml.
According to embodiments of the invention the coefficient of the expression value of the
CRP protein in the linear combination is from about 0.003/CRPmin L/mg to about
0.003/CRPmin L/mg, the being defined as log(pmax/(1-pmax)) - log(pmin/(1-pmin)), the pmax being
more than 0.9 and less than 1, the pmin being from about 0.01 to about 0.5, and the CRPmin being
from about 0 to about 1.5 mg/L.
According to yet another aspect of the present invention there is provided a method of
diagnosing a non-symptomatic subject with a viral infection, comprising measuring the level of a
determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of the
subject, wherein when the level of TRAIL is greater than 100 pg/ml, it is indicative that the non-
symptomatic subject has a viral infection, and/or wherein when the level of IP10 is greater than
200 pg/ml, it is indicative that the non-symptomatic subject has a viral infection.
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According to further embodiments, the method further comprises measuring the protein
level of CRP in the body liquid of the subject, wherein when the level of CRP is greater than 5
mg/ml, it is indicative that the non-symptomatic subject has a viral infection.
According to further embodiments, the method further comprises measuring the level of
IP10 and CRP in the body liquid of the subject.
According to yet another aspect of the present invention there is provided a method of
diagnosing a non-symptomatic subject with a viral infection, comprising measuring the level of a
determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of the
subject, wherein when the level of TRAIL is at least 10 % higher than a control subject that does
not have an infection, it is indicative that the non-symptomatic subject has a viral infection, and/or
wherein when the level of IP10 is at least 25 % higher than a control subject that does not have an
infection, it is indicative that the non-symptomatic subject has a viral infection.
According to further embodiments, when the level of TRAIL is at least 25 % higher than a
control subject that does not have an infection, it is indicative that the non-symptomatic subject
has a viral infection.
According to further embodiments, when the IP10 is at least 50 % higher than a control
subject that does not have an infection, it is indicative that the non-symptomatic subject has a viral
infection.
According to further embodiments, the non-symptomatic subject is contagious.
According to further embodiments, the method further comprises treating the non-
symptomatic subject who has been diagnosed as having the viral infection with an anti-viral agent,
anti-viral regimen, hospital admittance, mechanical ventilation, invasive monitoring, last-resort
drug, sedation, intensive care admission, surgical intervention, hospital admittance and isolation.
According to further embodiments, the method further comprises isolating the non-
symptomatic subject who has been diagnosed with the viral infection.
According to further embodiments, a virus of the viral infection is selected from the group
consisting of Adenovirus, Bocavirus, Coronavirus, Enterovirus, Influenza virus,
Metapneumovirus, Parainfluenza virus, Respiratory syncytial virus and Rhinovirus.
According to further embodiments, the method further comprises measuring the level of at
least one determinant set forth in Tables 1, 2 or 7.
According to further embodiments, the method further comprises measuring measuring the
level of at least one RNA determinant set forth in Table 12.
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According to further embodiments, the method further comprises measuring further
comprising measuring the level of at least one determinant selected from the group consisting of
PCT, IL-6 and CRP.
According to yet another aspect of the present invention there is provided a method of
diagnosing a non-symptomatic subject with a viral infection, comprising measuring the level of at
least one pair of determinants set forth in Tables 8, 9 or 24 or a triplet of determinants set forth in
Tables 10, 11 or 25 in a body liquid sample of the subject, wherein when the levels of determinants
are statistically significantly altered compared to the levels in a subject who does not have an
infection, it is indicative that the non-symptomatic subject has a viral infection.
According to yet another aspect of the present invention there is provided a method of
diagnosing a subject that has been infected by a coronavirus, comprising measuring the TRAIL
protein level in a body liquid sample of the subject, wherein the level is indicative whether the
subject has been infected by the coronavirus.
According to yet another aspect of the present invention there is provided a method of
diagnosing a subject that has been infected by a coronavirus, comprising measuring the IP10
protein level in a body liquid sample of the subject, wherein the level is indicative whether the
subject has been infected by the coronavirus.
Unless otherwise defined, all technical and/or scientific terms used herein have the same
meaning as commonly understood by one of ordinary skill in the art to which the invention
pertains. Although methods and materials similar or equivalent to those described herein can be
used in the practice or testing of embodiments of the invention, exemplary methods and/or
materials are described below. In case of conflict, the patent specification, including definitions,
will control. In addition, the materials, methods, and examples are illustrative only and are not
intended to be necessarily limiting.
Implementation of the method and/or system of embodiments of the invention can involve
performing or completing selected tasks manually, automatically, or a combination thereof.
Moreover, according to actual instrumentation and equipment of embodiments of the method
and/or system of the invention, several selected tasks could be implemented by hardware, by
software or by firmware or by a combination thereof using an operating system.
For example, hardware for performing selected tasks according to embodiments of the
invention could be implemented as a chip or a circuit. As software, selected tasks according to
embodiments of the invention could be implemented as a plurality of software instructions being
executed by a computer using any suitable operating system. In an exemplary embodiment of the
invention, one or more tasks according to exemplary embodiments of method and/or system as
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described herein are performed by a data processor, such as a computing platform for executing a
plurality of instructions. Optionally, the data processor includes a volatile memory for storing
instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or
removable media, for storing instructions and/or data. Optionally, a network connection is provided
as well. A display and/or a user input device such as a keyboard or mouse are optionally provided
as well.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
Some embodiments of the invention are herein described, by way of example only, with
reference to the accompanying drawings. With specific reference now to the drawings in detail, it
is stressed that the particulars shown are by way of example and for purposes of illustrative
discussion of embodiments of the invention. In this regard, the description taken with the drawings
makes apparent to those skilled in the art how embodiments of the invention may be practiced.
In the drawings:
FIGs. 1A-C – TRAIL (Figure 1A), IP-10 (Figure 1B) and CRP (Figure 1C) serial
measurements on a non-symptomatic patient. Plot indicates the % increase from basal level. The
hour 0 represents the time of symptoms onset. TRAIL, IP-10 and CRP were shown to increase
prior to symptom onset.
FIGs. 2A-B. TRAIL (Figure 2A) and IP-10 (Figure 2B) levels in individuals with high and
low viral loads. Both protein levels correlate with viral load status in non-symptomatic patients, a
surrogate for patient infectivity status.
FIG.3 is a graph of the IP10 levels of a Covid 19 patient throughout the course of the
disease.
FIG. 4 is an overview of dynamic clinical decision support protocol employed for
managing SARS-CoV-2 patients admitted to a COVID-19 dedicated medical center (Rabin
Medical Center, HaSharon, Israel). P/F ratio, Ratio of arterial oxygen partial pressure (PaO2) to
fractional inspired oxygen (FiO2); US, Ultrasound; CT, Computed tomography; NICaS, Non-
Invasive Cardiac system; NO, Nitric oxide; TPA, Tissue plasminogen activator; ECMO,
Extracorporeal membrane oxygenation; CRP, C-reactive protein; IP-10, interferon-γ induced
protein 10 (also known as CXCL-10); Plasm Abs, Plasm antibodies; TRAIL, TNF-related
apoptosis inducing ligand.
FIG. 5 is a graph illustrating that initiation of corticosteroid therapy was reflected by a
decrease in IP-10 levels (ICU patients, n = 12). IP-10 was measured using MeMed Key™ at the
indicated time points during the stay of the patients in the ICU. Day 0 = initiation of corticosteroid
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therapy. Pink area indicates IP-10 ≥ 1,000 pg/ml. Patients #2, #8 and #11 exhibited subsequent
surges in IP-10 expression and are detailed in Figures 4A-C.
FIGs. 6A-C are graphs illustrating that IP-10 levels reflect personalized corticosteroid
dosing (both increase and decrease). These 3 patients had a relapse of IP-10 levels >1,000 pg/ml,
after initial treatment with corticosteroids. Right Y-axis shows the normalized levels of
corticosteroids administered (hydrocortisone was converted to methylprednisolone). Left Y-axis
shows the levels of IP-10 measured by MeMed Key™ at the indicated time points. X-axis shows
the days from the first positive SARS-CoV-2 PCR. Lines connecting the doses indicate that the
dose was given in each of the intervening day. Pink area indicates IP-10 > 1,000 pg/ml. Patient 2
survived, patients 8 and 11 died.
FIG. 7 is a Flow Diagram of Study Population for Derivation Cohort (as exemplified in
Example 6).
FIGs. 8A-B are Kaplan-Meier Survival Curves of 14-day Mortality Across COVID-19
Severity algorithm Score Bins according to embodiments of the present invention compared to
other markers.
FIG. 9 is a graph illustrating the AUROC Performance of COVID-19 Severity algorithm
according to embodiments of the present invention compared to other markers.
FIG. 10 is a flowchart diagram of a method suitable for analyzing biological data obtained
from a subject, according to various exemplary embodiments of the present invention.
FIGs. 11 is a schematic illustration describing a procedure for calculating a distance of a
curved line from an axis according to some embodiments of the present invention.
FIGs. 12A-D are schematic illustrations describing a procedure for obtaining the smooth
version of a segment of a curved line, according to some embodiments of the present invention.
FIG. 13 is a schematic illustration of a client-server computer configuration according to
some embodiments of the present invention.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
The present invention, in some embodiments thereof, relates to methods of diagnosing and
classifying the severity of viral infections and more specifically to coronaviral infections.
Before explaining at least one embodiment of the invention in detail, it is to be understood
that the invention is not necessarily limited in its application to the details of construction and the
arrangement of the components and/or methods set forth in the following description and/or
illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or
of being practiced or carried out in various ways.
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In most SARS-CoV-2-positive patients, a localized, short-lasting immune response is
sufficient to clear the virus from the lungs, following which the immune response recedes and the
patient recovers. However, in as many as 14% of patients, a dysregulated immune response ensues,
triggering a hyperinflammatory state that can lead to acute lung injury, acute respiratory distress
syndrome (ARDS), multiple organ failure, and mortality. The identification of markers or patterns
that can be used to help stratify the risk of COVID-19 disease progression would improve the
outcome of infected individuals and enable better resource allocation, thereby relieving the burden
on the US healthcare system. In this way, individuals likely to deteriorate can be provided with
higher level care, and those unlikely to progress to poor outcomes can have care de-escalated.
Current prognostic tools for infection severity have one or more of the following
limitations: the scoring systems are complex and/or subjective and require data rich input;
individual biomarkers predominantly relate to bacterial infection and were derived based on
elderly cohorts; and the severity tools were not validated for early identification of the potential to
deteriorate. Notably, the seemingly unusual pathophysiology of SARS-CoV-2 infection may
require a dedicated tool for stratifying the likelihood of poor outcomes.
The present inventor have now shown that a multiplex host-protein signature
encompassing proteins from distinct biological pathways has potential to serve as a rapid, objective
and accurate triage test for early detection of severe SARS-CoV-2 infection outcomes. The blood
levels of each of TRAIL, CRP and IP10 have been shown to be differentially responsive to the
severity of infection, and specifically to the severity of COVID-19 infection (Figures 8A-B and
Figure 9).
Thus, according to a first aspect of the present invention there is provided a method of
classifying the severity of a coronaviral infection of a subject, comprising measuring the TRAIL
and/or IP10 protein level in a body liquid sample of the subject, wherein the level is indicative of
the severity of the coronaviral infection.
A “subject” in the context of the present invention may be a mammal (e.g. human, dog, cat,
horse, cow, sheep, pig or goat). According to another embodiment, the subject is a bird (e.g.
chicken, turkey, duck or goose). According to a particular embodiment, the subject is a human.
The subject may be male or female. The subject may be an adult (e.g. older than 18, 21, or 22
years or a child (e.g. younger than 18, 21 or 22 years). In another embodiment, the subject is an
adolescent (between 12 and 21 years), an infant (29 days to less than 2 years of age) or a neonate
(birth through the first 28 days of life).
For this aspect of the invention, the subject may present with symptoms of the coronavirus
infection (e.g. fever, cough, sore throat) or not present with symptoms of the coronavirus infection.
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Examples of coronaviruses include: human coronavirus 229E, human coronavirus OC43,
SARS-CoV, HCoV NL63, HKU1, MERS-CoV and SARS-CoV-2.
According to a particular embodiment, the coronavirus is SARS-CoV-2.
In one embodiment, the severity test is carried out on a subject who has been pre-diagnosed
as having a coronaviral infection. In one embodiment, the severity test is carried out no more than
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14 days following confirmation of the coronaviral infection. In
another embodiment, the severity test is carried out on a subject who does not yet show symptoms
of a severe infection.
The term “determining the severity” refers to as assignment of the severity of the disease
which may in one embodiment, relate to the probability to experience certain adverse events (e.g.
death, hospitalization or admission to ICU) to an individual. Hereby, the individual may be
accounted to a certain severity category, wherein categories comprise for instance high severity
versus low severity, or severity categories based on numeral values, such as severity category 1,
2, 3, 4, 5 etc.
In one embodiment, a subject who is categorized as having a severe coronaviral infection
is one predicted to die, require ICU, require ventilation and/or require Extracorporeal membrane
oxygenation (ECMO) treatment within 14 days of the test. In another embodiment, the subject
who is categorized as having a severe coronaviral infection is one that will require high glow nasal
cannul (HFNC), bilevel positive airway pressure (BPAP) treatment, or continues positive air
pressure (CPAP) support.
A subject who is categorized as having a non severe coronaviral infection is one (according
to one embodiment) that is predicted not to die from the disease, not to require ICU, not to require
ventilation and/or not to require Extracorporeal membrane oxygenation (ECMO) treatment within
14 days of the test. In another embodiment, the subject who is categorized as having a non-severe
coronaviral infection is one that will not require high flow nasal cannul (HFNC) or bilevel positive
airway pressure (BPAP) treatment, or continues positive air pressure (CPAP) support.
Additional deteminants that can be used in conjuction with TRAIL and IP10 to classify the
severity of the coronaviral disease are set forth in Table 1. The level of these determinants can be
measured at the protein or RNA level. Non-protein determinants that can be used in conjuction
with TRAIL and IP10 to classify the severity of the coronaviral disease are set forth in Table 2.
Table 1
TRAIL
IP-10
CRP
IL-6
PCT
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Pro-adrenomedullin
IL1R/ IL1R1/ IL1RA
SAA/ SAA1
TREM1
TREM2
RSAD2
NGAL
IL1R/ IL1R1/ IL1RA
IL-1-beta
SAA/ SAA1
SAA/L
MMP8
MX1
Neopterin
Presepsin
TNF-alpha
IL-10
IL-7
MCP-1
MCP-3
MIP-1-alpha
G-CSF
Table 2
WBC
ANC
% Lymphocytes
% Neutrophils
Neutrophil-to-Lymphocyte Ratio
Fibrinogen
Triglyceride
D-dimer
Ferritin
AST
ALT
PCR-Ct (viral shedding levels)
% Saturation
PaO2/FiO2 ratio
According to a specific embodiment, the TRAIL, IP10, CRP, IL-6 and/or PCT protein level
is used to stratify the subject having a coronaviral disease into one of four levels of likelihood for
severe outcome – very low, low, moderate and high.
According to a specific embodiment, the TRAIL, IP10 and CRP protein level is used to
stratify the subject having a coronaviral disease into one of four levels of likelihood for severe
outcome – very low, low, moderate, high.
According to a specific embodiment, TRAIL, IP10 and CRP are measured to classify the
severity of a SARS-CoV-2 infection.
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As mentioned, when a patient has been diagnosed as having a severe infection, the
management course is typically more aggressive than if he had not been assessed as having a severe
infection. Thus, treatment options (for a respiratory disease) such as mechanical ventilation, life
support, catheterization, hemofiltration, invasive monitoring, sedation, intensive care admission,
surgical intervention, drug of last resort and hospital admittance may be selected which may
otherwise not have been considered the preferred method of treatment if the patient had not been
assessed as having a severe respiratory infection.
Treatment of a subject classified as having a respiratory infection with very low likelihood
for severe outcome may include oxygen administration (between 1-6 L/minute). The FiO of the
2
inhaled gas is typically between 24-50 %. This may be carried out using a nasal cannula.
Treatment of a subject classified as having a respiratory infection with low likelihood for
severe outcome may include oxygen administration (between 5-10 L/minute). The FiO of the
2
inhaled gas is typically between 40-60 %. This may be carried out using a nasal cannula or a
simple face mask.
Treatment of a subject classified as having a respiratory infection with moderate likelihood
for severe outcome may include oxygen administration (about 15 L/minute). The FiO of the
2
inhaled gas is typically between 60-90 %. This may be carried out using a reservoir mask.
Treatment of a subject classified as having a respiratory infection with high likelihood for
severe outcome may include oxygen administration (up to 70 L/minute). The FiO2 of the inhaled
gas is up to 100 %. This may be carried out using nasal high flow oxygen or CPAP. Treatment
may further include intubation and ECMO.
Other exemplary treatments that can be used once the classification has been made include
oxygen therapy, non-invasive ventilation, mechanical ventilation, invasive monitoring, last-resort
drug, sedation, intensive care admission, surgical intervention, hospital admittance, anti-viral drug,
anti-viral regimen, anti-fungal drug, immune-globulin treatment, glucocorticoid therapy,
extracorporeal membrane oxygenation, kidney replacement therapy and isolation.
Exemplary agents that may be used to treat coronavirus (e.g. SARS-CoV-2) infections
include an antiviral agent, immunomodulatorty therapy (e.g. corticosteroid), an IV fluid, a
vasopressor, an inotrope and a diuretic.
Secondary infections due to coronavirus can be treated using antibiotic or other antibacterial
agents.
In one embodiment, the severity assessment is made in the emergency department of a
hospital.
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Emergency departments (ED) are progressively overwhelmed by patients with both urgent
and non-urgent problems. This leads to overfilled ED waiting rooms with long waiting times,
detrimental outcomes and unsatisfied patients. As a result, patients needing urgent care may not be
treated in time, whereas patients with non-urgent problems may unnecessarily receive expensive
and dispensable treatments. Time to effective treatment is among the key predictors for outcomes
across different medical conditions, including patients with septicaemia, pneumonia, stroke and
myocardial infarction. For these reasons, the present inventors propose use of the presently
disclosed severity stratification system in the ED is essential for an optimal initial triage of medical
patients.
In another embodiment, the severity assessment is made in the internal ward of a hospital.
In another embodiment, the severity assessment is made in the intensive care unit of a
hospital.
The severity measurement may be used to determine a management course for the patient.
The severity measurement may aid in selection of treatment priority and also site-of-care decisions
(i.e. outpatient vs. inpatient management) and early identification and organization of post-acute
care needs.
The present inventors have further shown that the levels of TRAIL and IP10 accurately
reflect the course of an immune-related or inflammation-related disease and continual monitoring
thereof aids in selection of alternative treatments throughout the disease.
According to another aspect of the present invention there is provided a method of managing
treatment of a disease which brings about a dysregulation of an immune and/or inflammatory
response of a subject, said subject already being medicated with a first treatment to treat said
disease comprising:
(a) measuring an amount of IP10 and/or TRAIL in a sample of the subject; and
(b) altering the treatment or treatment course when the level of IP10 is above a
predetermined threshold and/or when the level of TRAIL is below a predetermined threshold in
said sample.
Examples of diseases which bring about a dysregulation of an immune and or inflammatory
response include but are not limited to an infectious disease, ulcerative colitis, Crohn’s disease,
rheumatoid arthritis, Sjogren syndrome, Kawasaki disease, Guillain-Barre syndrome, lupus,
asthma, COPD, Multiple sclerosis, vasculitis and psoriasis.
In one embodiment, the disease is acute respiratory distress syndrome (ARDS).
In one embodiment, the infectious disease is a viral infection.
Exemplary viruses that cause disease include those set forth in Table 3 herein below.
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Table 3
Family Baltimore group Important species envelopment
Group I non-
Adenoviridae Adenovirus
(dsDNA) enveloped
Herpes simplex, type 1, Herpes simplex, type 2,
Group I
Herpesviridae Varicella-zoster virus, Epstein–Barr virus, Human enveloped
(dsDNA)
cytomegalovirus, Human herpesvirus, type 8
Group I non-
Papillomaviridae Human papillomavirus
(dsDNA) enveloped
Group I non-
Polyomaviridae BK virus, JC virus
(dsDNA) enveloped
Group I
Poxviridae Smallpox enveloped
(dsDNA)
Group VII
Hepadnaviridae Hepatitis B virus enveloped
(dsDNA-RT)
Group II non-
Parvoviridae Parvovirus B19
(ssDNA) enveloped
Group IV
non-
Astroviridae (positive-sense Human astrovirus
enveloped
ssRNA)
Group IV
non-
Caliciviridae (positive-sense Norwalk virus
enveloped
ssRNA)
Group IV
coxsackievirus, hepatitis A virus, poliovirus, non-
Picornaviridae (positive-sense
rhinovirus enveloped
ssRNA)
Group IV
Coronaviridae (positive-sense Severe acute respiratory syndrome virus enveloped
ssRNA)
Group IV
Hepatitis C virus, yellow fever virus, dengue virus,
Flaviviridae (positive-sense enveloped
West Nile virus, TBE virus
ssRNA)
Group IV
Togaviridae (positive-sense Rubella virus enveloped
ssRNA)
Group IV
non-
Hepeviridae (positive-sense Hepatitis E virus
enveloped
ssRNA)
Group VI
Retroviridae Human immunodeficiency virus (HIV) enveloped
(ssRNA-RT)
Group V
Orthomyxoviridae (negative-sense Influenza virus enveloped
ssRNA)
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Baltimore group envelopment
Family Important species
Group V
Arenaviridae (negative-sense Lassa virus enveloped
ssRNA)
Group V
Crimean-Congo hemorrhagic fever virus, Hantaan
Bunyaviridae (negative-sense enveloped
virus
ssRNA)
Group V
Filoviridae (negative-sense Ebola virus, Marburg virus enveloped
ssRNA)
Group V
Measles virus, Mumps virus, Parainfluenza virus,
Paramyxoviridae (negative-sense enveloped
Respiratory syncytial virus,
ssRNA)
Group V
Rhabdoviridae (negative-sense Rabies virus enveloped
ssRNA)
Group V
Unassigned (negative-sense Hepatitis D enveloped
ssRNA)
Group III non-
Reoviridae Rotavirus, Orbivirus, Coltivirus, Banna virus
(dsRNA) enveloped
According to a specific embodiment, the virus is SARS-CoV-2.
According to another specific embodiment, the virus is Human metapneumovirus,
Bocavirus or Enterovirus.
Exemplary viral diseases which can be managed according to this aspect of the present
invention include those set forth in Table 4, herein below.
Table 4
Diseases
gastroenteritis
keratoconjunctivitis
pharyngitis
croup
pharyngoconjunctival fever
pneumonia
cystitis
Hand, foot and mouth disease
pleurodynia
aseptic meningitis
pericarditis
myocarditis
infectious mononucleosis
Burkitt's lymphoma
Hodgkin's lymphoma
nasopharyngeal carcinoma
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Diseases
acute hepatitis
chronic hepatitis
hepatic cirrhosis
hepatocellular carcinoma
herpes labialis, cold sores - can recur by latency
gingivostomatitis in children
tonsillitis & pharyngitis in adults
keratoconjunctivitis
Aseptic meningitis
infectious mononucleosis
Cytomegalic inclusion disease
Kaposi sarcoma
multicentric Castleman disease
primary effusion lymphoma
AIDS
influenza
(Reye syndrome)
measles
postinfectious encephalomyelitis
mumps
hyperplastic epithelial lesions (common, flat, plantar and anogenital warts, laryngeal
papillomas, epidermodysplasia verruciformis
Malignancies for some species (cervical carcinoma, squamous cell carcinomas)
croup
pneumonia
bronchiolitis
common cold[
poliomyelitis
rabies (fatal encephalitis)
congenital rubella
German measles
chickenpox
herpes zoster
Congenital varicella syndrome
Particular examples of viruses that cause viral infections are respiratory viruses including
but not limited to RSV, Flu A, Flu B, HCoV and SARS-Cov-2 and those described herein above.
In a particular embodiment, the disease which is managed according to this aspect of the
present invention is COVID-19.
In another embodiment, the infectious disease which can be managed according to this
aspect of the present invention is a bacterial infection.
The subjects of this aspect of the present invention are described herein above.
In one embodiment, the subject is hospitalized (e.g, in the ICU). In another embodiment,
the subject is not hospitalized.
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The subjects of this aspect of the present invention are already being treated for the disease.
The type of treatment which the subject is being treated is dependent on the particular
disease.
In one embodiment, the subject is already being treated with an immunosuppressant.
Exemplary immunosuppressants are provided in Table 5, herein below:
Table 5
In another embodiment, the subject is already being treated with an immunostimulator,
examples of which are provided in Table 6, herein below.
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Table 6
In yet another embodiment, the subject whose disease is being managed according to this
aspect of the present invention is in a cytokine storm and is already being treated with a suitable
agent (e.g. a steroid agent).
According to this aspect of the present invention, the present inventors contemplate
measuring the level (e.g. the expression) of TRAIL and/or IP10 and on the basis of their levels
altering the treatment which is already in use for treatment of the subject.
The TRAIL and IP-10 may be measured on the RNA level or the protein level.
Methods of measuring TRAIL or IP10 are described herein below.
Thus, in one embodiment, the subject is already being medicated with a first therapeutic
agent and on the basis of the level of TRAIL and/or IP10, administration of a second therapeutic
agent is administered. The first therapeutic agent may be stopped or the two agents may be
administered together.
In another embodiment, the subject is being medicated with a first therapeutic agent and
on the basis of the level of TRAIL and/or IP10, the dose or treatment regimen using the first
therapeutic agent is altered.
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The TRAIL and/or IP10 level may be monitored more than one time throughout the course
of the disease - e.g. once a week, once every two days, once a day, twice a day, every 12 hours,
every 6 hours, every 3 hours. In another embodiment, the level of TRAIL and or IP/10 is
monitored continuously (e.g. in real time).
According to another embodiment, the TRAIL and/or IP10 level is monitored at least 12
hours after the start of a new treatment, at least 6 hours, at least 3 hours or even 1 hour after the
start of the new treatment.
In one embodiment, when the IP10 has increased above 1000 pg/ml, 1200 pg/ml, 1500
pg/ml, 1750 pg/ml, 2000 pg/ml, 2500 pg/ml, it is indicative that a new treatment or dosing is called
for.
In another embodiment, when the TRAIL level has decreased below 25 pg/ml, it is
indicative that a new treatment or dosing is called for.
In still another embodiment, the level of IP10 together with the level of TRAIL is measured
at least twice a day, the IP10 level providing information on the immune status of the subject, and
the TRAIL level providing information on the amount of virus present in the subject. The present
inventors envisage balancing these two parameters to bring about optimal treatment for the subject.
As well as determining the level of IP10 and/or TRAIL, the present inventors further
contemplate analyzing the length of time these determinants are above their relevant thresholds.
Thus, for IP10, the present inventors contemplate analyzing for how long its levels are
above 1000 pg/ml or 1500 pg/ml. If the length of time is beyond a predetermined length (e.g. 1
day, two days, three days, four days etc.), it is indicative of a worst prognosis and a more extreme
response.
For TRAIL, the present inventors contemplate analyzing for how long its levels are below
pg/ml. If the length of time is beyond a predetermined length (e.g. 1 day, two days, three days,
four days etc.), it is indicative of a worst prognosis and a more extreme response.
The immune system has an exquisite mechanism capable of responding to various
pathogens. Normal anti-viral immune response requires the activation of the inflammatory
pathways of the immune system; however, aberrant or exaggerated response of the host's immune
system can cause severe disease if remains uncontrolled. Cytokines are an essential part of the
inflammatory process. During an innate immune response to a viral infection, inflammatory
cytokines, together with chemokines and adhesion molecules aid to recruit leukocytes and plasma
proteins to the site of infection where they perform various effector functions that serve to combat
the triggering infection.
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The cytokine storm results from a sudden acute increase in circulating levels of different
pro-inflammatory cytokines including IL-6, IL-1, TNF- α and interferon. This increase in cytokines
results in influx of various immune cells such as macrophages, neutrophils, and T cells from the
circulation into the site of infection with destructive effects on human tissue resulting from
destabilization of endothelial cell to cell interactions, damage of vascular barrier, capillary damage,
diffuse alveolar damage, multiorgan failure, and ultimately death. Lung injury is one consequence
of the cytokine storm that can progress into acute lung injury or its more severe form ARDS.
The present inventors propose that the level of IP10 can be used as a gauge for predicting
the onset of a cytokine storm.
According to yet another aspect of the present invention there is provided a method of
predicting onset of a cytokine storm in a subject having a disease which brings about a
dysregulation of an immune and/or inflammatory response, the method comprising:
(a) measuring an amount of IP10 in a sample of the subject; and
(b) predicting a cytokine storm based on the level of IP10.
In one embodiment, the cytokine storm is the result of an immune-based therapy selected
from the group consisting of Tumor-Infiltrating Lymphocyte (TIL) Therapy, Engineered T Cell
Receptor (TCR) Therapy, Chimeric Antigen Receptor (CAR) T Cell Therapy and Natural Killer
(NK) Cell Therapy.
Diseases which are known to bring about cytokine storms include but are not limited to
infectious disease (e.g. viral infections), Ulcerative colitis, Crohn’s disease, Rheumatoid arthritis,
Sjogren syndrome, Kawasaki disease, Guillain-Barre syndrome, Lupus, Asthma, COPD, Multiple
sclerosis, vasculitis, Psoriasis and graft-vs-host disease (GVHD).
According to a particular embodiment, the viral disease which may bring about a cytokine
storm is a respiratory viral disease, such as those transmitted via an influenza virus, Ebola,
smallpox, or a coronavirus (e.g. severe acute respiratory syndrome coronavirus (SARS-CoV-2)
and Middle East respiratory syndrome coronavirus (MERS-CoV).
Additional proteins which may be used to predict a cytokine storm include, but are not
limited to IL-6, PCT and CRP. In another embodiment, TRAIL used together with IP10 to predict
the onset of a cytokine storm.
Additional determinants which may be used to predict a cytokine storm include, but are
not limited to those set forth in Tables 1 or 2.
In one embodiment, when the IP10 has increased above 1000 pg/ml, 1200 pg/ml, 1500
pg/ml, 1750 pg/ml, 2000 pg/ml, 2500 pg/ml, it is indicative that the subject is about to be in
cytokine storm.
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In one embodiment, a cytokine storm is defined as an increase in at least three, four, five,
six, seven or all of the following cytokines: interleukin (IL)-6, IL-2, IL-7, granulocyte colony
stimulating factor, interferon-γ induced protein 10, monocyte chemoattractant protein 1,
macrophage inflammatory protein 1-α, and tumor necrosis factor-α.
The predicting may be carried out 1 hour before the event, 2 hours before the event, 3
hours before the event, 6 hours before the event, 12 hours before the event or even 24 hours before
the event.
The immune system serves in part to protect organisms from infectious pathogens.
However, during infection, effector functions, such as cellular or antibody mediated cytotoxicity,
and inflammatory cytokines can be harmful to the host. To limit damage to healthy tissue,
mechanisms have evolved to shut down immune responses, including immune cell inactivation
and cell death. This is termed a “system shutdown”. In contrast, a cytokine storm is the excessive,
uncontrolled release of pro-inflammatory proteins.
The present inventors further propose that TRAIL may be used as a marker for
distinguishing between a system shutdown diagnosis and a cytokine storm diagnosis.
Thus, according to another aspect of the present invention there is provided a method of
distinguishing between a system shutdown diagnosis and a cytokine storm diagnosis in a subject
with a viral disease, comprising measuring the expression level of TRAIL in a sample of the
subject, wherein when said level of TRAIL is above a predetermined level, a cytokine storm
diagnosis is ruled in and/or when said level of TRAIL is below a predetermined level, a system
shutdown diagnosis is ruled in.
To distinguish between system shutdown and cytokine storm, the present inventors further
contemplate analyzing the level of at least one additional determinant set forth in Tables 1 or 2,
herein above.
The subject for whom the classification is being carried out typically presents with
symptoms of an infectious disease (e.g. fever, cough, sputum production, myalgia, fatigue,
headache, anorexia, dyspnea, diarrhea, nausea, dizziness, headache, vomiting, abdominal pain,
sore throat, nasal congestion, hemoptysis and chills).
Viruses and viral diseases which induce system shutdown or cytokine storms are listed in
Tables 3 and 4 herein above.
In a particular embodiment, the subject is infected with a coronavirus (e.g. SARS-CoV-2).
According to yet another aspect of the present invention there is provided a method of
determining the severity of a viral infection of a subject, comprising measuring the expression
level of a first determinant set forth in Table 1 and the level of a clinical measurement set forth in
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Table 2 in a sample of the subject, wherein said levels are indicative of the severity of the viral
infection.
The subject of this aspect of the present invention has typically been diagnosed with a viral
infection.
In one embodiment, the subject has been diagnosed with a particular viral disease - e.g. a
respiratory viral disease such as COVID-19.
Other viruses and viral diseases are disclosed herein above in Tables 3 and 4, herein above.
Methods of diagnosing viral diseases include for example clinical assessment, PCR
analysis, sequencing analysis, viral culture, antibody or antigen testing, or by use of host immune
response measurements.
The subject may be symptomatic for the viral disease or non-symptomatic for the viral
disease.
The subject may be contagious or non-contagious.
In one embodiment, the subject is hospitalized. In another embodiment, the subject is not
hospitalized.
According to some embodiments of the invention, the method further comprises informing
the subject of results of the severity testing.
As used herein the phrase “informing the subject” refers to advising the subject that based
on the diagnosis the subject should seek a suitable treatment regimen.
Once the diagnosis is determined, the results can be recorded in the subject’s medical file,
which may assist in selecting a treatment regimen and/or determining prognosis of the subject.
As mentioned above, once the severity of the virus has been determined, the subject may be
treated in a particular way, as further detailed herein above.
Exemplary managements for viral infections include, but are not limited to oxygen therapy,
non-invasive ventilation, mechanical ventilation, invasive monitoring, last-resort drug, sedation,
intensive care admission, surgical intervention, hospital admittance, anti-viral drug, anti-viral
regimen, anti-fungal drug, immune-globulin treatment, glucocorticoid therapy, extracorporeal
membrane oxygenation, kidney replacement therapy and isolation.
Exemplary agents that may be used to treat coronavirus (e.g. SARS-CoV-2) infections
include an antiviral agent, an IV fluid, a vasopressor, an inotrope and a diuretic.
The antiviral drug may be selected from the group consisting of remdesivir, an interferon,
ribavirin, adefovir, tenofovir, acyclovir, brivudin, cidofovir, fomivirsen, foscarnet, ganciclovir,
penciclovir, amantadine, rimantadine and zanamivir.
Also contemplated are plasma treatments from infected persons who survived and/or anti-
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HIV drugs such as lopinavir and ritonavir, as well as chloroquine.
Specific examples for drugs that are routinely used for the treatment of COVID-19 include,
but are not limited to, Lopinavir /Ritonavir, Nucleoside analogues, Neuraminidase inhibitors,
Remdesivir, polypeptide (EK1), abidol, RNA synthesis inhibitors (such as TDF, 3TC), anti-
inflammatory drugs (such as hormones and other molecules), Chinese traditional medicine, such
ShuFengJieDu Capsules and Lianhuaqingwen Capsule, could be the drug treatment options for
COVID19.
The present inventors contemplate that at least two determinants are used to determine
severity of the viral disease.
According to another embodiment, at least three determinants are used to rule in severity
of the disease.
Exemplary combinations for this aspect of the present invention are set forth herein below.
TRAIL and IL-6;
TRAIL and %lymph;
TRAIL, PCT and IL-6;
TRAIL, CRP and IL-6;
TRAIL, IP-10 and IL-6TRAIL, IL-6 and % Lymph;
TRAIL, IL-6, % Lymph and PCT;
TRAIL, IL-6, PCT and CRP.
IP-10 and IL-6
IP-10 and PCT
IP-10, PCT and IL-6
According to a particular embodiment, TRAIL and IP10 are measured.
According to a particular embodiment, TRAIL and IL-6 are measured.
According to a particular embodiment, TRAIL and PCT are measured.
According to still another embodiment, TRAIL and CRP are measured
According to still another embodiment, TRAIL and % Lymphocytes are measured
According to a particular embodiment, TRAIL, CRP and IP10 are measured.
According to a particular embodiment, TRAIL, IL-6 and %Lymph are measured.
According to another embodiment, TRAIL, IL-6, % Lymph and PCT are measured.
According to another embodiment, TRAIL, CRP, IP10 and IL-6 are measured.
According to another embodiment, TRAIL, CRP, IP10, IL-6 and PCT are measured.
According to a particular embodiment, TRAIL and IP10 are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
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According to another embodiment, CRP and IP10 are measured together with at least one
determinant set forth in Table 1 to determine the severity of the viral infection.
According to another embodiment, CRP and IL6 are measured together with at least one
determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, CRP and PCT are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, TRAIL and CRP are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, TRAIL and IL-6 are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, IL-6 and IP-10 are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, TRAIL and PCT are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, CRP and PCT are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to still another embodiment, IP-10 and PCT are measured together with at least
one determinant set forth in Table 1 to determine the severity of the viral infection.
According to a particular embodiment, TRAIL, CRP and IP10 are measured together with
at least one determinant set forth in Table 1 to determine the severity of the viral infection.
According to a particular embodiment, TRAIL, CRP and IL-6 are measured together with
at least one determinant set forth in Table 1 to determine the severity of the viral infection.
According to a particular embodiment, TRAIL, IP-10 and IL-6 are measured together with
at least one determinant set forth in Table 1 to determine the severity of the viral infection.
According to another embodiment, TRAIL, CRP, IP10 and PCT are measured together with
at least one determinant set forth in Table 1 to determine the severity of the viral infection.
According to another embodiment, TRAIL, CRP, IP10 and IL-6 are measured together with
at least one determinant set forth in Table 1 to determine the severity of the viral infection.
According to another embodiment, TRAIL, CRP, IP10, IL-6 and PCT are measured
together with at least one determinant set forth in Table 1 to determine the severity of the viral
infection.
Virally infected individuals are often contagious prior to onset of clinical symptoms, and
sometimes even in their absence. Currently, these contagious yet asymptomatic individuals are
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unidentifiable in real-time, and can only be detected if symptoms manifest, when they have already
potentially infected others.
The present inventors have now shown that particular determinants (e.g. TRAIL, CRP and
IP10) and combinations thereof exhibit a differential pattern between non-virally infected subjects
and virally infected subjects at “time 0”, wherein "time 0" is defined as the time of symptom onset.
At “time 0” these determinants are already highly elevated in response to infection. Accordingly,
the present inventors propose that these determinants and others are upregulated prior to day 0,
and therefore can be used to distinguish between non-virally infected subjects and virally infected
subjects at the non-symptomatic phase of their infection. These markers were shown to accurately
distinguish between these two states for a myriad of different virus types including, but not limited
to influenza, RSV and Coronavirus, across different age groups, clinical syndromes, etc.
In addition, serial measurements of TRAIL and IP-10 were performed on a non-
symptomatic individual who had been in close contact with a virally infected patient. On day 3 the
patient developed upper respiratory tract symptoms. TRAIL and IP-10 levels were shown to
increase from basal level prior to symptoms onset. More specifically, a 25% and 50% increase in
TRAIL and IP-10 levels, respectively, were found to accurately detect the non-symptomatic
individual prior to symptoms onset (Figures 1A-B).
The present inventors propose that knowledge of viral infection at the non-symptomatic
stage (e.g. prior to fever onset) will help guide triage of infectious/infected individuals in
hospital/health care settings. Furthermore, this knowledge can be used to flag non-symptomatic
individuals in public settings such as border-control/airport/train station etc. In addition, the
present invention can be used for providing ongoing infectivity and infectious monitoring for
healthcare/public servants/other individuals with high exposure to infectious diseases, as well as
to guide treatment regimens e.g. antiviral treatment (with/without relation to infective status).
Lastly, the ability to diagnose viral infections at the non-symptomatic stage allows for the
stratification of individuals according to likelihood of viral infection and therefore will aid in
quarantine/segregation decisions.
Thus, according to still another aspect of the present invention there is provided a method
of diagnosing a non-symptomatic subject with a viral infection, comprising measuring the level of
a determinant selected from the group consisting of TRAIL, IP10, CRP, IL-6 and PCT in a body
liquid sample of the subject, wherein the level is indicative whether the non-symptomatic subject
has a viral infection.
According to a particular embodiment, TRAIL and IP10 are measured in the non-
symptomatic subject.
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According to another embodiment, CRP and IP10 are measured in the non-symptomatic
subject.
According to another embodiment, CRP and IL6 are measured in the non-symptomatic
subject.
According to still another embodiment, CRP and PCT are measured in the non-symptomatic
subject.
According to still another embodiment, TRAIL and CRP are measured in the non-
symptomatic subject.
According to still another embodiment, TRAIL and IL-6 are measured in the non-
symptomatic subject.
According to still another embodiment, IL-6 and IP-10 are measured in the non-
symptomatic subject.
According to still another embodiment, TRAIL and PCT are measured in the non-
symptomatic subject.
According to still another embodiment, CRP and PCT are measured in the non-symptomatic
subject.
According to still another embodiment, IP-10 and PCT are measured in the non-
symptomatic subject.
According to a particular embodiment, TRAIL, CRP and IP10 are measured in the non-
symptomatic subject.
According to a particular embodiment, TRAIL, CRP and IL-6 are measured in the non-
symptomatic subject.
According to a particular embodiment, TRAIL, IP-10 and IL-6 are measured in the non-
symptomatic subject.
According to another embodiment, TRAIL, CRP, IP10 and PCT are measured in the non-
symptomatic subject.
According to another embodiment, TRAIL, CRP, IP10 and IL-6 are measured in the non-
symptomatic subject.
According to another embodiment, TRAIL, CRP, IP10, IL-6 and PCT are measured in the
non-symptomatic subject.
The term “non-symptomatic” according to this aspect of the present invention refers to a
subject that does not have a fever, but is afflicted with the viral disease. The non-symptomatic
subject may be in the early phase of the disease and may later show other clinical symptoms of the
disease, such as fever. This subject may or may not be contagious. Alternatively, the non-
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symptomatic subject may be one that never develops other clinical symptoms of the disease, yet
is contagious. This subject may be in the early, middle or late stages of the disease. Still
alternatively, the subject may be in the late stages (e.g. recovery stage) of the disease, where his
fever has already gone down to normal, but is still contagious.
The non-symptomatic subject may or may not show minor symptoms of a viral disease.
In one embodiment, the non-symptomatic subject, as well as not having fever, does not
show respiratory symptoms - e.g. coughing, sputum production, sore throat or nasal congestion.
In another embodiment, the non-symptomatic subject, as well as not having fever, presents
with at least one respiratory symptom.
According to still another embodiment, the non-symptomatic subject as well as not having
fever, presents with none, one, two, three or four of the below symptoms: cough, sputum
production, myalgia, fatigue, headache, anorexia, dyspnea, diarrhea, nausea, dizziness, headache,
vomiting, abdominal pain, sore throat, nasal congestion, hemoptysis and chills.
The non-symptomatic subject may be contagious or non-contagious.
In another embodiment, a non-symptomatic subject may be diagnosed as having a viral
infection if the level of TRAIL is at least 25 %, 30 %, 35 %, 40 %, 45 %, or 50 % higher than a
control subject who does not have an infection.
According to a particular embodiment, the control level of TRAIL (based on data from non-
infected subjects) is about 75 pg/ml.
According to another embodiment, the control level of TRAIL may be the level of TRAIL
of that subject when he is healthy (e.g. not under any suspicion of having an infectious disease).
According to another embodiment, the control level of TRAIL may be the average level of
TRAIL of a group of at least 100 healthy subjects (e.g. subject who are not under any suspicion of
having an infectious disease, do not have an immune related disorder and/or do not have a cardiac
disorder).
A non-symptomatic subject may be diagnosed as having a viral infection if the level of IP10
is at least 50 % higher than a control subject who does not have an infection.
According to a particular embodiment, the control level of IP10 (based on data from non-
infected subjects) is about 90 pg/ml.
The control level of IP10 may be the level of IP10 of that subject when he is healthy (e.g.
not under any suspicion of having an infectious disease).
According to another embodiment, the control level of IP10 may be the average level of
IP10 of a group of at least 100 healthy subjects (e.g. subject who are not under any suspicion of
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having an infectious disease, do not have an immune related disorder and/or do not have a cardiac
disorder).
In one embodiment, the level of CRP in a body liquid sample of the subject is indicative
whether the non-symptomatic subject has a viral infection.
For example, when the level of CRP is greater than 5 mg/ml, 6 mg/ml, 7 mg/ml, 8 mg/ml,
9 mg/ml, 10 mg/ml, 11 mg/ml, 12 pg/ml, 13 pg/ml, 14 mg/ml, 15 mg/ml, 16 mg/ml, 17 mg/ml, 18
mg/ml, 19 mg/ml, 20 mg/ml, it is indicative that the non-symptomatic subject has a viral infection.
For example, a non-symptomatic subject may be diagnosed as having a viral infection if the
level of CRP is at least 50 % higher than a control subject who does not have an infection.
The control level of CRP may be the level of CRP of that subject when he is healthy (e.g.
not under any suspicion of having an infectious disease).
According to another embodiment, the control level of CRP may be the average level of
CRP of a group of at least 100 healthy subjects (e.g. subject who are not under any suspicion of
having an infectious disease, do not have an immune related disorder and/or do not have a cardiac
disorder).
According to a specific embodiment, the control level of CRP (based on data from non-
infectious subjects) is about 5 mg/ml.
As used herein, the term “diagnosing” refers to determining presence or absence of the virus,
monitoring virus progression, forecasting an outcome of a pathology and/or prospects of recovery
and/or screening of a subject for the virus.
According to some embodiments of the invention, diagnosing of the subject for the virus
is followed by substantiation of the screen results using gold standard methods.
For example, once a non-symptomatic subject has been diagnosed as having a virus, the
viral infection can be substantiated using a clinical assessment, PCR analysis, sequencing analysis,
viral culture, antibody or antigen testing, or by use of host immune response measurements.
According to some embodiments of the invention, the method further comprises informing
the subject of the diagnosis.
Exemplary viruses that cause disease and the diseases themselves are provided in Tables 3
and 4 herein above.
According to a specific embodiment, the viral disease which is diagnosed in the non-
symptomatic subject is a respiratory disease such as COVID-19.
The diagnosis of a non-symptomatic subject with a viral disease may be used to make
decisions as to whether the subject should be isolated or not (i.e isolated from subjects who do not
have the disease). In addition, the diagnosis can be used to determine what treatment should be
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used. Such a treatment may include use of an anti-viral agent or an anti-viral regime such as those
described herein above. The diagnosis may further be used to determine when the subject should
be retested.
Additional determinants that can be used to rule in a viral disease in a non-symptomatic
subject are listed in Tables 1 and 2, herein above and Table 7, herein below.
Table 7- singlets: data derived from the comparison of viral patients tested at time from
symptoms <=1 (day 0 or day 1) to non-infectious patients. The analysis was performed on
proprietary data.
Feature #1 AUC Feature #1 AUC
GDF15 1 PCSK9 0.686
IP-10 0.961 a1 Acid 0.686
Glycoprotein
MBL 0.914 Angiopoietin2 0.657
Neopterin 0.914 Dkk1 0.657
CRP 0.896 SLPI 0.657
CD27 0.857 Vitamin D 0.657
Binding Protein
MMP2 0.857 Angiogenin 0.629
Resistin 0.857 CD95 0.629
IL-6 0.84 Cystatin C 0.6
RSAD2 0.827 Endostatin 0.6
MX1 0.813 LIGHT 0.6
Tie2 0.8 MIF 0.6
VCAM1 0.8 RBP4 0.6
CD14 0.771 PCT 0.589
IGFBP3 0.743 Complement 0.571
factor D
Thrombospondin2 0.743 Osteoprotegerin 0.571
PTEN 0.735 uPAR 0.571
ICAM1 0.714 Fetuin A 0.543
APRIL 0.686 Leptin 0.514
Clusterin 0.686 IL7R 0.489
IL18 0.686 CXCL13 0.486
IL19 0.686 GCP2 0.457
IL1R 0.686 Adiponectin 0.371
P Selectin 0.686
The present inventors have also uncovered particular protein pairs and triplets which
should be able to detect viruses in non-symptomatic subjects.
In one embodiment, the pair of determinants which is used to rule in a viral infection in a
non-symptomatic subject is set forth in Table 8 and the triplet of determinants is set forth in Table
.
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Table 8- pairs: data derived from the comparison of viral patients tested at time from
symptoms <=1 (day 0 or day 1) to non-infectious patients. The analysis was performed on
proprietary data.
Feature #1 Feature #2 AUC
IL-6 RSAD2 1
APRIL IP-10 1
Adiponectin IP-10 1
Angiogenin IP-10 1
Angiopoietin2 IP-10 1
CD27 IP-10 1
CD95 IP-10 1
Clusterin GDF15 1
Complement factor D IP-10 1
Cystatin C IP-10 1
Dkk1 IP-10 1
Endostatin IP-10 1
Fetuin A IP-10 1
Fetuin A MMP2 1
GCP2 IP-10 1
GDF15 IGFBP3 1
GDF15 MIF 1
GDF15 Osteoprotegerin 1
GDF15 TRAIL 1
GDF15 uPAR 1
ICAM1 IP-10 1
IGFBP3 IP-10 1
IL19 IP-10 1
IL1R IP-10 1
IP-10 LIGHT 1
IP-10 Leptin 1
IP-10 MIF 1
IP-10 Neopterin 1
IP-10 Osteoprotegerin 1
IP-10 P Selectin 1
IP-10 RBP4 1
IP-10 Resistin 1
IP-10 Thrombospondin2 1
IP-10 Tie2 1
IP-10 VCAM1 1
IP-10 Vitamin D Binding Protein 1
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IP-10 uPAR 1
P Selectin Resistin 1
Resistin TRAIL 1
MX1 TRAIL 0.99
IP-10 RSAD2 0.988
CD27 TRAIL 0.971
CXCL13 IP-10 0.971
Clusterin IP-10 0.971
GDF15 IL1R 0.971
GDF15 MBL 0.971
IL18 IP-10 0.971
IP-10 PCSK9 0.971
LIGHT Resistin 0.971
MIF VCAM1 0.971
Neopterin Thrombospondin2 0.971
TRAIL uPAR 0.971
CRP IP-10 0.968
IP-10 PCT 0.968
IL-6 IP-10 0.967
CRP RSAD2 0.944
CD14 IP-10 0.943
Dkk1 GDF15 0.943
GDF15 Neopterin 0.943
IGFBP3 VCAM1 0.943
MMP2 Resistin 0.943
IL7R IP-10 0.94
CRP IP-10 0.937
CRP TRAIL 0.937
IP-10 TRAIL 0.935
CRP TRAIL 0.932
Adiponectin GDF15 0.929
GDF15 IP-10 0.929
GDF15 P Selectin 0.929
IP-10 MBL 0.929
PCT RSAD2 0.924
CRP IL-6 0.915
Angiopoietin2 GDF15 0.914
Cystatin C GDF15 0.914
Fetuin A Neopterin 0.914
GDF15 IL18 0.914
GDF15 Leptin 0.914
GDF15 Tie2 0.914
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IGFBP3 Neopterin 0.914
IP-10 SLPI 0.914
IP-10 a1 Acid Glycoprotein 0.914
IP-10 PTEN 0.906
APRIL Resistin 0.9
CD95 TRAIL 0.9
IP-10 MX1 0.899
CD95 GDF15 0.886
CRP GDF15 0.886
Fetuin A GDF15 0.886
GDF15 IL19 0.886
GDF15 MMP2 0.886
GDF15 VCAM1 0.886
GDF15 Vitamin D Binding Protein 0.886
IGFBP3 MBL 0.886
IP-10 MMP2 0.886
MBL TRAIL 0.886
MMP2 TRAIL 0.886
RSAD2 TRAIL 0.877
IL-6 TRAIL 0.876
CRP PCT 0.875
GDF15 ICAM1 0.871
ICAM1 Thrombospondin2 0.871
IL19 Neopterin 0.871
MX1 PTEN 0.87
IL-6 MX1 0.869
IP-10 TRAIL 0.865
CRP IL7R 0.859
Angiogenin GDF15 0.857
Clusterin MBL 0.857
GCP2 GDF15 0.857
GDF15 PCSK9 0.857
CXCL13 GDF15 0.843
LIGHT Neopterin 0.843
CD27 GDF15 0.829
GDF15 RBP4 0.829
Neopterin TRAIL 0.829
TRAIL Tie2 0.829
Complement factor D GDF15 0.814
Neopterin Resistin 0.814
SLPI TRAIL 0.814
TRAIL a1 Acid Glycoprotein 0.814
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PTEN TRAIL 0.813
IL-6 PCT 0.805
IL7R MX1 0.804
CD27 ICAM1 0.8
LIGHT MMP2 0.8
CRP MX1 0.788
IL-6 PTEN 0.781
IL7R RSAD2 0.779
MX1 RSAD2 0.779
PTEN RSAD2 0.775
MX1 PCT 0.773
CD14 Clusterin 0.771
CD14 GDF15 0.771
GDF15 LIGHT 0.771
GDF15 SLPI 0.771
GDF15 Thrombospondin2 0.771
GDF15 a1 Acid Glycoprotein 0.771
ICAM1 MBL 0.771
ICAM1 VCAM1 0.771
Neopterin Vitamin D Binding Protein 0.757
IL-6 IL7R 0.744
IL7R PCT 0.731
APRIL GDF15 0.729
Clusterin VCAM1 0.729
Endostatin GDF15 0.729
ICAM1 TRAIL 0.729
IL7R TRAIL 0.705
CD14 TRAIL 0.7
GDF15 Resistin 0.7
IL7R PTEN 0.687
PCT TRAIL 0.678
CRP PTEN 0.602
PCT PTEN 0.5
In one embodiment, at least one of the members of the pairs is PCT, TRAIL, IL-6 or CRP.
Such pairs are summarized in Table 9 herein below.
Table 9
Feature #1 Feature #2 AUC- new Sensitivity Specificity PPV NPV
IL-6 RSAD2 1 1 1 1 1
GDF15 TRAIL 1 1 1 1 1
Resistin TRAIL 1 1 1 1 1
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MX1 TRAIL 0.99 1 0.909 0.9 1
CD27 TRAIL 0.971 0.857 1 1 0.833
TRAIL uPAR 0.971 0.857 1 1 0.833
CRP IP-10 0.968 0.895 0.944 0.879 0.952
IP-10 PCT 0.968 0.911 0.936 0.879 0.953
IL-6 IP-10 0.967 0.947 0.928 0.857 0.975
CRP RSAD2 0.944 0.889 1 1 0.9
CRP IP-10 0.937 0.889 1 1 0.875
CRP TRAIL 0.937 0.889 1 1 0.875
IP-10 TRAIL 0.935 0.895 0.896 0.797 0.949
CRP TRAIL 0.932 0.877 0.928 0.847 0.943
PCT RSAD2 0.924 0.875 0.889 0.875 0.889
CRP IL-6 0.915 0.825 0.888 0.77 0.917
CD95 TRAIL 0.9 0.857 1 1 0.833
CRP GDF15 0.886 0.857 0.8 0.857 0.8
MBL TRAIL 0.886 1 0.8 0.875 1
MMP2 TRAIL 0.886 0.857 0.8 0.857 0.8
RSAD2 TRAIL 0.877 0.778 1 1 0.818
IL-6 TRAIL 0.876 0.789 0.936 0.849 0.907
CRP PCT 0.875 0.786 0.853 0.733 0.886
IL-6 MX1 0.869 0.889 0.909 0.889 0.909
IP-10 TRAIL 0.865 0.889 0.857 0.889 0.857
CRP IL7R 0.859 0.889 0.846 0.8 0.917
Neopterin TRAIL 0.829 0.714 1 1 0.714
TRAIL Tie2 0.829 0.857 0.8 0.857 0.8
SLPI TRAIL 0.814 0.857 0.8 0.857 0.8
TRAIL a1 Acid 0.814 0.714 0.8 0.833 0.667
Glycoprotein
PTEN TRAIL 0.813 0.875 0.75 0.778 0.857
IL-6 PCT 0.805 0.75 0.927 0.84 0.878
CRP MX1 0.788 0.778 1 1 0.846
IL-6 PTEN 0.781 0.875 0.875 0.875 0.875
MX1 PCT 0.773 0.75 0.818 0.75 0.818
IL-6 IL7R 0.744 0.778 0.923 0.875 0.857
IL7R PCT 0.731 0.875 0.615 0.583 0.889
ICAM1 TRAIL 0.729 0.857 0.6 0.75 0.75
IL7R TRAIL 0.705 0.778 0.846 0.778 0.846
CD14 TRAIL 0.7 0.857 0.6 0.75 0.75
PCT TRAIL 0.678 0.607 0.78 0.586 0.794
CRP PTEN 0.602 0.625 0.875 0.833 0.7
PCT PTEN 0.5 0.625 0.5 0.556 0.571
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Exemplary triples of proteins that can be used to rule in a viral infection of non-
symptomatic patients are disclosed in Table 10, herein below.
Table 10- triples: data derived from the comparison of viral patients tested at time from
symptoms <=1 (day 0 or day 1) to non-infectious patients. The analysis was performed on
proprietary data.
Feature #1 Feature #2 Feature #3 AUC Sensitivity Specificity PPV NPV
CRP IL7R TRAIL 1 1 1 1 1
CRP PTEN TRAIL 1 1 1 1 1
CRP RSAD2 TRAIL 1 1 1 1 1
IL-6 IL7R RSAD21 111 1
IL-6 MX1 RSAD21 111 1
IL-6 PTEN RSAD21 111 1
IL7R IP-10 PCT 0.99 1 0.923 0.889 1
IP-10 RSAD2 TRAIL 0.988 0.889 1 1 0.9
IL-6 PTEN TRAIL 0.984 0.875 1 1 0.889
CRP IL-6 IL7R 0.983 1 0.923 0.9 1
IL-6 IL7R TRAIL 0.983 0.889 1 1 0.929
CRP IP-10 PCT 0.974 0.929 0.936 0.881 0.962
CRP IL-6 IP-10 0.973 0.93 0.952 0.898 0.967
IP-10 PCT PTEN 0.969 0.875 1 1 0.889
IP-10 PTEN TRAIL 0.969 1 0.875 0.889 1
IL-6 IP-10 PCT 0.966 0.929 0.927 0.867 0.962
IL-6 IP-10 TRAIL 0.952 0.93 0.888 0.791 0.965
IP-10 PCT TRAIL 0.951 0.893 0.927 0.862 0.944
MX1 PCT TRAIL 0.949 1 0.909 0.889 1
CRP IP-10 TRAIL 0.945 0.877 0.928 0.847 0.943
CRP IL-6 RSAD2 0.944 0.889 1 1 0.9
CRP MX1 RSAD2 0.944 0.889 1 1 0.9
CRP MX1 TRAIL 0.944 0.889 1 1 0.917
IL-6 RSAD2 TRAIL 0.944 0.889 1 1 0.9
IL7R MX1 TRAIL 0.944 1 0.9 0.9 1
IL7R PCT TRAIL 0.942 0.875 0.923 0.875 0.923
CRP IL7R RSAD2 0.938 0.889 1 1 0.889
CRP PTEN RSAD2 0.938 0.875 1 1 0.889
IL-6 IP-10 RSAD2 0.938 0.889 1 1 0.9
IP-10 MX1 PCT 0.938 0.875 0.909 0.875 0.909
MX1 RSAD2 TRAIL 0.938 1 0.889 0.9 1
CRP IL-6 TRAIL 0.936 0.895 0.888 0.785 0.949
IL-6 MX1 TRAIL 0.934 0.889 1 1 0.917
IP-10 MX1 RSAD2 0.932 0.889 1 1 0.9
IL-6 PCT RSAD2 0.931 1 0.889 0.889 1
IP-10 PCT RSAD2 0.931 0.875 1 1 0.9
IP-10 PTEN RSAD2 0.922 1 0.875 0.889 1
CRP IL7R IP-10 0.919 0.889 0.846 0.8 0.917
IL7R RSAD2 TRAIL 0.917 0.889 0.875 0.889 0.875
CRP PCT TRAIL 0.915 0.839 0.954 0.904 0.92
CRP IL-6 PCT 0.903 0.839 0.835 0.723 0.91
IL7R IP-10 MX1 0.889 1 0.8 0.818 1
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IP-10 MX1 TRAIL 0.879 0.778 1 1 0.846
CRP IP-10 RSAD2 0.877 0.778 1 1 0.818
IL-6 PCT PTEN 0.875 1 0.75 0.8 1
IL7R IP-10 TRAIL 0.872 0.778 1 1 0.867
CRP PCT RSAD2 0.868 0.875 0.889 0.875 0.889
IL7R IP-10 RSAD2 0.868 0.889 0.75 0.8 0.857
IL-6 PCT TRAIL 0.866 0.786 0.899 0.8 0.891
IL7R MX1 PTEN 0.865 0.87 0.9 0.952 0.75
MX1 PCT RSAD2 0.861 0.875 0.778 0.778 0.875
MX1 PTEN TRAIL 0.859 0.875 0.875 0.875 0.875
CRP IL7R MX1 0.856 0.778 1 1 0.833
CRP IL7R PCT 0.851 0.875 0.846 0.778 0.917
CRP IL-6 PTEN 0.844 0.75 1 1 0.8
PCT PTEN RSAD2 0.844 0.75 0.875 0.857 0.778
IL-6 IP-10 MX1 0.843 0.778 0.909 0.875 0.833
CRP IP-10 PTEN 0.828 0.75 0.875 0.857 0.778
PTEN RSAD2 TRAIL 0.828 0.75 0.875 0.857 0.778
CRP IP-10 MX1 0.818 0.667 1 1 0.786
IP-10 MX1 PTEN 0.813 0.75 0.875 0.857 0.778
MX1 PTEN RSAD2 0.81 0.957 0.727 0.88 0.889
CRP IL-6 MX1 0.798 0.778 0.818 0.778 0.818
IL-6 IL7R PCT 0.798 0.75 1 1 0.867
IL7R PCT RSAD2 0.797 0.875 0.75 0.778 0.857
IL-6 MX1 PCT 0.795 0.75 1 1 0.846
CRP MX1 PTEN 0.789 0.625 1 1 0.727
IL7R MX1 PCT 0.788 0.75 0.8 0.75 0.8
IL7R PTEN TRAIL 0.786 0.75 0.857 0.857 0.75
PCT RSAD2 TRAIL 0.785 0.75 0.778 0.75 0.778
CRP MX1 PCT 0.773 0.75 1 1 0.846
CRP IL7R PTEN 0.768 0.625 0.857 0.833 0.667
IL-6 IL7R MX1 0.767 0.889 0.8 0.8 0.889
IL-6 MX1 PTEN 0.758 0.75 0.875 0.857 0.778
IL7R MX1 RSAD2 0.75 0.844 0.692 0.871 0.643
PCT PTEN TRAIL 0.75 0.875 0.625 0.7 0.833
IL7R IP-10 PTEN 0.732 0.75 0.857 0.857 0.75
IL-6 IL7R IP-10 0.722 0.778 0.923 0.875 0.857
IL7R PTEN RSAD2 0.713 0.696 0.7 0.842 0.5
MX1 PCT PTEN 0.703 0.75 0.75 0.75 0.75
CRP PCT PTEN 0.688 0.625 0.75 0.714 0.667
IL-6 IP-10 PTEN 0.648 0.625 1 1 0.727
IL-6 IL7R PTEN 0.643 0.75 0.714 0.75 0.714
IL7R PCT PTEN 0.643 1 0.571 0.727 1
According to a particular embodiment, at least one of the triplet members is PCT, TRAIL,
IL-6 or CRP.
Exemplary triplets which include at least one of the above mentioned determinants are set
forth in Table 11, herein below.
Table 11
Feature Feature Feature AUC Sensitivity Specificity PPV NPV
#1 #2 #3
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CRP IL7R TRAIL 1 1 1 1 1
CRP PTEN TRAIL 1 1 1 1 1
CRP RSAD2 TRAIL 1 1 1 1 1
IL-6 IL7R RSAD2 1 1 1 1 1
IL-6 MX1 RSAD2 1 1 1 1 1
IL-6 PTEN RSAD2 1 1 1 1 1
IL7R IP-10 PCT 0.99 1 0.923 0.889 1
IP-10 RSAD2 TRAIL 0.988 0.889 1 1 0.9
IL-6 PTEN TRAIL 0.984 0.875 1 1 0.889
CRP IL-6 IL7R 0.983 1 0.923 0.9 1
IL-6 IL7R TRAIL 0.983 0.889 1 1 0.929
CRP IP-10 PCT 0.974 0.929 0.936 0.881 0.962
CRP IL-6 IP-10 0.973 0.93 0.952 0.898 0.967
IP-10 PCT PTEN 0.969 0.875 1 1 0.889
IP-10 PTEN TRAIL 0.969 1 0.875 0.889 1
IL-6 IP-10 PCT 0.966 0.929 0.927 0.867 0.962
IL-6 IP-10 TRAIL 0.952 0.93 0.888 0.791 0.965
IP-10 PCT TRAIL 0.951 0.893 0.927 0.862 0.944
MX1 PCT TRAIL 0.949 1 0.909 0.889 1
CRP IP-10 TRAIL 0.945 0.877 0.928 0.847 0.943
CRP IL-6 RSAD2 0.944 0.889 1 1 0.9
CRP MX1 RSAD2 0.944 0.889 1 1 0.9
CRP MX1 TRAIL 0.944 0.889 1 1 0.917
IL-6 RSAD2 TRAIL 0.944 0.889 1 1 0.9
IL7R MX1 TRAIL 0.944 1 0.9 0.9 1
IL7R PCT TRAIL 0.942 0.875 0.923 0.875 0.923
CRP IL7R RSAD2 0.938 0.889 1 1 0.889
CRP PTEN RSAD2 0.938 0.875 1 1 0.889
IL-6 IP-10 RSAD2 0.938 0.889 1 1 0.9
IP-10 MX1 PCT 0.938 0.875 0.909 0.875 0.909
MX1 RSAD2 TRAIL 0.938 1 0.889 0.9 1
CRP IL-6 TRAIL 0.936 0.895 0.888 0.785 0.949
IL-6 MX1 TRAIL 0.934 0.889 1 1 0.917
IL-6 PCT RSAD2 0.931 1 0.889 0.889 1
IP-10 PCT RSAD2 0.931 0.875 1 1 0.9
CRP IL7R IP-10 0.919 0.889 0.846 0.8 0.917
IL7R RSAD2 TRAIL 0.917 0.889 0.875 0.889 0.875
CRP PCT TRAIL 0.915 0.839 0.954 0.904 0.92
CRP IL-6 PCT 0.903 0.839 0.835 0.723 0.91
IP-10 MX1 TRAIL 0.879 0.778 1 1 0.846
CRP IP-10 RSAD2 0.877 0.778 1 1 0.818
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IL-6 PCT PTEN 0.875 1 0.75 0.8 1
IL7R IP-10 TRAIL 0.872 0.778 1 1 0.867
CRP PCT RSAD2 0.868 0.875 0.889 0.875 0.889
IL-6 PCT TRAIL 0.866 0.786 0.899 0.8 0.891
MX1 PCT RSAD2 0.861 0.875 0.778 0.778 0.875
MX1 PTEN TRAIL 0.859 0.875 0.875 0.875 0.875
CRP IL7R MX1 0.856 0.778 1 1 0.833
CRP IL7R PCT 0.851 0.875 0.846 0.778 0.917
CRP IL-6 PTEN 0.844 0.75 1 1 0.8
PCT PTEN RSAD2 0.844 0.75 0.875 0.857 0.778
IL-6 IP-10 MX1 0.843 0.778 0.909 0.875 0.833
CRP IP-10 PTEN 0.828 0.75 0.875 0.857 0.778
PTEN RSAD2 TRAIL 0.828 0.75 0.875 0.857 0.778
CRP IP-10 MX1 0.818 0.667 1 1 0.786
CRP IL-6 MX1 0.798 0.778 0.818 0.778 0.818
IL-6 IL7R PCT 0.798 0.75 1 1 0.867
IL7R PCT RSAD2 0.797 0.875 0.75 0.778 0.857
IL-6 MX1 PCT 0.795 0.75 1 1 0.846
CRP MX1 PTEN 0.789 0.625 1 1 0.727
IL7R MX1 PCT 0.788 0.75 0.8 0.75 0.8
IL7R PTEN TRAIL 0.786 0.75 0.857 0.857 0.75
PCT RSAD2 TRAIL 0.785 0.75 0.778 0.75 0.778
CRP MX1 PCT 0.773 0.75 1 1 0.846
CRP IL7R PTEN 0.768 0.625 0.857 0.833 0.667
IL-6 IL7R MX1 0.767 0.889 0.8 0.8 0.889
IL-6 MX1 PTEN 0.758 0.75 0.875 0.857 0.778
PCT PTEN TRAIL 0.75 0.875 0.625 0.7 0.833
IL-6 IL7R IP-10 0.722 0.778 0.923 0.875 0.857
MX1 PCT PTEN 0.703 0.75 0.75 0.75 0.75
CRP PCT PTEN 0.688 0.625 0.75 0.714 0.667
IL-6 IP-10 PTEN 0.648 0.625 1 1 0.727
IL-6 IL7R PTEN 0.643 0.75 0.714 0.75 0.714
IL7R PCT PTEN 0.643 1 0.571 0.727 1
RNA markers that can be used to rule in a viral infection of asymptomatic patients. The
genes were selected based on their ability to separate day 0-1 viral patients from non-infectious
patients or were detected early in a viral challenge study.
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Table 12
Gene RRefSeq mRNAOC Sensitivity Specificity Fold
AUC change
RSAD2 NM_080657.5 1.00 1.00 1.00 232.8
IFI27 NM_001130080.3 0.99 0.93 1.00 196.1
IFI44L NM_001375646.1 1.00 1.00 1.00 183.8
SERPING1 NM_000062.3 1.00 1.00 1.00 94.4
IFI44 NM_006417.5 1.00 1.00 1.00 90.0
IFIT1 NM_001270927.2 0.98 1.00 0.92 87.0
OAS3 NM_006187.4 1.00 1.00 1.00 86.0
HERC5 NM_016323.4 0.98 1.00 0.92 60.9
USP18 NM_017414.4 0.91 0.86 1.00 53.1
CMPK2 NM_207315.4 1.00 1.00 1.00 47.6
CARD17 NM_001007232.1 1.00 1.00 1.00 38.9
EPSTI1 NM_001002264.4 1.00 1.00 1.00 35.6
MX1 NM_001144925.2 0.99 1.00 0.92 32.7
OAS2 NM_016817.3 0.96 0.93 1.00 32.1
IFI6 NM_022872.3 0.99 0.93 1.00 29.3
OASL NM_003733.3 1.00 1.00 1.00 26.8
DDX60 NM_017631.6 1.00 1.00 1.00 25.4
OAS1 NM_016816.4 1.00 1.00 1.00 24.7
IFIT3 NM_001549.6 0.99 0.93 1.00 24.1
LY6E NM_002346.3 0.90 0.86 1.00 20.3
IFIH1 NM_022168.4 1.00 1.00 1.00 16.5
PLSCR1 NM_001363872.1 0.98 1.00 0.92 16.4
IFI35 NM_005533.5 1.00 1.00 1.00 16.2
LAP3 NM_015907.3 1.00 1.00 1.00 15.6
IFIT2 NM_001547.5 0.96 0.93 0.92 15.0
XAF1 NM_017523.5 1.00 1.00 1.00 14.6
ANKRD22 NM_144590.3 1.00 1.00 1.00 12.4
RTP4 NM_022147.3 1.00 1.00 1.00 12.2
IFIT5 NM_012420.3 0.99 0.93 1.00 11.9
SAMD9L NM_152703.5 1.00 1.00 1.00 11.2
PARP12 NM_022750.4 1.00 1.00 1.00 11.1
GBP1 NM_002053.3 1.00 1.00 1.00 11.1
TNFAIP6 NM_007115.4 0.96 0.79 1.00 10.9
AIM2 NM_004833.3 1.00 1.00 1.00 10.8
EIF2AK2 NM_002759.4 1.00 1.00 1.00 10.3
ZBP1 NM_030776.3 1.00 1.00 1.00 9.6
ETV7 NM_016135.4 1.00 1.00 1.00 9.6
DDX58 NM_014314.4 0.99 0.93 1.00 8.8
STAT2 NM_005419.4 1.00 1.00 1.00 8.7
GBP1P1 AK309962.1 1.00 1.00 1.00 8.3
ZCCHC2 XM_011539452.3 0.99 1.00 0.92 8.2
PARP14 XM_011512928.3 1.00 1.00 1.00 7.3
DDX60L XM_005263341.4 0.96 0.93 1.00 7.3
SAMD9 NM_152703.5 1.00 1.00 1.00 6.8
PARP9 NM_031458.3 1.00 1.00 1.00 6.7
ISG15 NM_005101.4 1.00 1.00 1.00 6.1
TNFSF10 NM_003810.4 0.95 0.79 1.00 3.3
JUP NM_001352773.2 0.83 0.86 0.85 2.8
OTOF NM_194248.3 1.00 1.00 1.00 1.9
EVI2A NM_001003927.3 0.51 0.36 1.00 1.1
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LAX1 NM_017773.4 0.71 0.57 0.92 -1.8
EMR3 XM_007489046.1 0.93 0.71 1.00 -5.9
CASS4 NM_001164116.2 0.97 0.93 1.00 -6.0
FAM101B NM_182705.2 0.89 0.86 1.00 -8.4
PHOSPHO1 NM_001143804.2 0.93 0.86 1.00 -10.6
PI3 NM_002638.4 0.98 0.93 1.00 -36.5
RNA pairs that can be used to rule in a viral infection of asymptomatic patients are
summarized in Table 24 herein below. The pairs were selected based on their ability to separate
day 0-1 viral patients from non-infectious patients or were detected early in a viral challenge study.
RNA triplets that can be used to rule in a viral infection of asymptomatic patients are summarized
in Table 25 herein below. The pairs were selected based on their ability to separate day 0-1 viral
patients from non-infectious patients or were detected early in a viral challenge study.
The present inventors have also identified signatures and determinants associated with viral
patient’s infectivity status. More specifically it was discovered that TRAIL and IP-10 have the
potential to indicate patient infectivity status as can be observed by the high correlation of protein
levels with viral load (the amount of virus in an organism; Figures 2A-B), a correlation of patient
infectivity status.
Thus, according to still another aspect of the present invention, there is provided a method
of analyzing the contagiousness of a non-symptomatic subject comprising measuring the level of
a determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of
the subject, wherein
(i) when said level of TRAIL is higher than the level of TRAIL in a subject who does not
have an infection, it is indicative that the non-symptomatic subject is contagious; and/or
(ii) when said level of IP10 is higher than the level of IP-10 in a subject who does not have
an infection, it is indicative that the non-symptomatic subject is contagious.
As used herein, the term “contagiousness” refers to the ability of a subject to infect other
subjects that are in close contact. The contagiousness of a particular subject will partly depend on
the infectivity of the virus itself, but also the viral load of the subject. It will be appreciated that
the subject may be symptomatic or non-symptomatic and level of contagiousness does not
necessarily depend on the amount or severity of symptoms a subject is suffering from.
The non-symptomatic subject may be one who is known to have been exposed or is
suspected of having been exposed to a subject who has the viral infection.
According to still another aspect of the present invention there is provided a method of
analyzing the degree of contagiousness of a test subject comprising measuring the level of a
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determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of the
subject, wherein the level of TRAIL and/or IP10 is proportional to the degree of contagiousness.
For this aspect, the test subject may be symptomatic or non-symptomatic.
Once a subject has been found to be contagious over a predetermined level, the subject may
be treated in a manner in which a subject found to be less contagious is treated - for example the
more contagious subject may be isolated, whereas the less contagious subject may not. The more
contagious subject may be treated with a particular treatment (e.g. anti-viral agent, mechanical
ventilation, invasive monitoring, last-resort drug, sedation, intensive care admission, surgical
intervention, hospital admittance), whereas the less contagious subject may not.
Additional protein determinants, pairs of protein determinants or triplets of protein
determinants that may be used to determine whether a subject is contagious or not is set forth in
any one of Tables 7-11, herein above. According to a particular embodiment, the determinant
appears in Table 7. According to another embodiment, the pair of determinants used to determine
the level of contagiousness appears in Table 8. According to still another embodiment, the triplet
of determinants used to determine the level of contagiousness appears in Table 10.
Additional RNA determinants that may be used to determine whether a subject is contagious
or not are set forth in Table 12 herein above.
According to still another aspect there is provided a method of diagnosing a subject that has
been infected by a coronavirus, comprising measuring the TRAIL or IP10 protein level in a body
liquid sample of the subject, wherein the level is indicative whether the subject has been infected
by the coronavirus. The diagnosing may also include classifying the severity of the coronavirus
infection.
For any of the aspects disclosed herein, the term “measuring” or “measurement,” or
alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or
amount (which can be an effective amount) of the determinant within a clinical or subject-derived
sample, including the derivation of qualitative or quantitative concentration levels of such
determinants.
A “sample” in the context of the present invention is a biological sample isolated from a
subject and can include, by way of example and not limitation, whole blood, serum, plasma, saliva,
mucus, breath, urine, CSF, sputum, sweat, stool, hair, seminal fluid, biopsy, rhinorrhea, tissue
biopsy, cytological sample, platelets, reticulocytes, leukocytes, epithelial cells, or whole blood
cells.
In a particular embodiment, the sample is a blood sample - e.g. serum, plasma, or whole
blood. The sample may be a venous sample, peripheral blood mononuclear cell sample or a
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peripheral blood sample. In one embodiment, the sample comprises white blood cells including
for example granulocytes, lymphocytes and/or monocytes. In one embodiment, the sample is
depleted of red blood cells.
The sample may be fresh or frozen.
According to a particular embodiment, the test subject of any of the aspects disclosed herein
does not show signs of having had a heart attack (e.g. has a normal level of creatine kinase,
troponin or serum myoglobin, and/or has a normal ECG or EKG).
According to yet another embodiment, the test subject does not have cancer.
Additional information concerning particular determinants is provided herein below.
CRP: C-reactive protein; additional aliases of CRP include without limitation RP11-
419N10.4 and PTX1.
An exemplary amino acid sequence of human CRP is set forth below in SEQ ID NO: 1.
TRAIL: The protein encoded by this gene is a cytokine that belongs to the tumor necrosis
factor (TNF) ligand family. The present invention contemplates measuring either the soluble
and/or the membrane form of this protein. In one embodiment, only the soluble form of this protein
is measured. Additional names of the gene include without limitations APO2L, TNF-related
apoptosis-inducing ligand, TNFSF10 and CD253. This protein binds to several members of the
TNF receptor superfamily such as TNFRSF10A/TRAILR1, TNFRSF10B/TRAILR2,
TNFRSF10C/TRAILR3, TNFRSF10D/TRAILR4, and possibly also to TNFRSF11B/OPG.
Exemplary amino acid sequences of TRAIL are set forth in SEQ ID NOs: 2 or 3.
IP10: This gene encodes a chemokine of the CXC subfamily and ligand for the receptor
CXCR3. Additional names of the gene include without limitations: CXCL10, Gamma-IP10,
INP10 and chemokine (C-X-C motif) ligand 10.
An exemplary amino acid sequence of human IP10 is set forth in SEQ ID NO: 4.
IL1RA: The protein encoded by this gene is a cytokine receptor that belongs to the
interleukin 1 receptor family. Additional names of the gene include without limitations: CD121A,
IL-1RT1, p80, CD121a antigen, CD121A, IL1R and IL1ra.
PCT: Procalcitonin (PCT) is a peptide precursor of the hormone calcitonin, the latter being
involved with calcium homeostasis.
TREM1: Triggering receptor expressed on myeloid cells 1; additional aliases of TREM1
are CD354 and TREM-1.
RSAD2: Radical S-adenosyl methionine domain containing 2; additional aliases of
RSAD2 include without limitation 2510004L01Rik, cig33, cig5 and vig1.
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MX1/MXA: myxovirus (influenza virus) resistance 1; additional aliases of MX1 include
without limitation IFI-78K, IFI78, MX and MxA.
TRAILR3/ TNFRSF10C: The protein encoded by this gene is a member of the TNF-
receptor superfamily.
Exemplary amino acid sequences of this protein are set forth in SEQ ID NOs: 5 or 6.
TRAILR4/ TNFRSF10D: The protein encoded by this gene is a member of the TNF-
receptor superfamily.
Exemplary amino acid sequences of this protein are set forth in SEQ ID NOs: 7 or 8.
TRAIL-R1/ TNFRSF10A: The protein encoded by this gene is a member of the TNF-
receptor superfamily. Examplary amino acid sequences of this protein are set forth in SEQ ID
NOs: 9, 10 or 11.
TRAIL-R2/ TNFRSF10B: The protein encoded by this gene is a member of the TNF-
receptor superfamily, and contains an intracellular death domain. Exemplary amino acid sequences
of this protein are set forth in SEQ ID NOs: 12, 13 or 14.
MMP8: Matrix metallproteinase 8 (MMP8) is a collagen cleaving enzyme. Exemplary
amino acid sequences of MMP8 are set forth in SEQ ID NOs: 16-18.
Neopterin: Neopterin is the catabolic product of guanosine triphosphate, a purine
nucleotide. Neopterin belongs to the chemical group known as pteridines.
IL-6: IL-6 is a Cytokine with a wide variety of biological functions. It is a potent inducer
of the acute phase response and plays an essential role in the final differentiation of B-cells.
Exemplary amino acid sequences of human IL-6 is set forth in SEQ ID NOs: 19-20.
GDF15: Growth Differentiation Factor 15 (GDF15) is expressed in a broad range of cell
types, acts as a pleiotropic cytokine and is involved in the stress response program of cells after
cellular injury. Additional aliases of GDF15: NSAID-Activated Gene 1 Protein and MIC-1.
APRIL: a member of the tumor necrosis factor (TNF) ligand family. This protein and its
receptor are both found to be important for B cell development. Additional aliases include
TNFSF13 and TALL2.
Methods of measuring the level of protein determinants are well known in the art and
include, e.g., immunoassays based on antibodies to proteins, aptamers or molecular imprints.
Protein determinants can be detected in any suitable manner, but are typically detected by
contacting a sample from the subject with an antibody, which binds the protein determinant and
then detecting the presence or absence of a reaction product. The antibody may be monoclonal,
polyclonal, chimeric, or a fragment of the foregoing, and the step of detecting the reaction product
may be carried out with any suitable immunoassay.
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In one embodiment, the antibody which specifically binds the determinant is attached
(either directly or indirectly) to a signal producing label, including but not limited to a radioactive
label, an enzymatic label, a hapten, a reporter dye or a fluorescent label.
Immunoassays carried out in accordance with some embodiments of the present invention
may be homogeneous assays or heterogeneous assays. In a homogeneous assay the immunological
reaction usually involves the specific antibody (e.g., anti- determinant antibody), a labeled analyte,
and the sample of interest. The signal arising from the label is modified, directly or indirectly,
upon the binding of the antibody to the labeled analyte. Both the immunological reaction and
detection of the extent thereof can be carried out in a homogeneous solution. Immunochemical
labels, which may be employed, include free radicals, radioisotopes, fluorescent dyes, enzymes,
bacteriophages, or coenzymes.
In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and
means for producing a detectable signal. Samples as described above may be used. The antibody
can be immobilized on a support, such as a bead (such as magnetic bead, protein A and protein G
agarose beads), plate, pipette tip or slide, and contacted with the specimen suspected of containing
the antigen in a liquid phase.
The support is then separated from the liquid phase and either the support phase or the
liquid phase is examined for a detectable signal employing means for producing such signal. The
signal is related to the presence of the analyte in the sample. Means for producing a detectable
signal include the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if
the antigen to be detected contains a second binding site, an antibody which binds to that site can
be conjugated to a detectable group and added to the liquid phase reaction solution before the
separation step. The presence of the detectable group on the solid support indicates the presence
of the antigen in the test sample. Examples of suitable immunoassays are oligonucleotides,
immunoblotting, immunofluorescence methods, immunoprecipitation, chemiluminescence
methods, electrochemiluminescence (ECL) or enzyme-linked immunoassays.
Those skilled in the art will be familiar with numerous specific immunoassay formats and
variations thereof which may be useful for carrying out the method disclosed herein. See generally
E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat.
No. 4,727,022 to Skold et al., titled “Methods for Modulating Ligand-Receptor Interactions and
their Application,” U.S. Pat. No. 4,659,678 to Forrest et al., titled “Immunoassay of Antigens,”
U.S. Pat. No. 4,376,110 to David et al., titled “Immunometric Assays Using Monoclonal
Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled “Macromolecular Environment
Control in Specific Receptor Assays,” U.S. Pat. No. 4,233,402 to Maggio et al., titled “Reagents
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and Method Employing Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled
“Heterogenous Specific Binding Assay Employing a Coenzyme as Label.” The determinant can
also be detected with antibodies using flow cytometry. Those skilled in the art will be familiar
with flow cytometric techniques which may be useful in carrying out the methods disclosed herein
(Shapiro 2005). These include, without limitation, Cytokine Bead Array (Becton Dickinson) and
Luminex technology.
Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads
such as magnetic bead, protein A or protein G agarose, microspheres, plates, pipette tips, slides or
wells formed from materials such as latex or polystyrene) in accordance with known techniques,
such as passive binding. Antibodies as described herein may likewise be conjugated to detectable
125 131
labels or groups such as radiolabels (e.g., S, I, I), enzyme labels (e.g., horseradish
peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green
fluorescent protein, rhodamine) in accordance with known techniques.
In particular embodiments, the antibodies of the present invention are monoclonal
antibodies.
Suitable sources for antibodies for the detection of determinants include commercially
available sources such as, for example, Abazyme, Abnova, AssayPro, Affinity Biologicals,
AntibodyShop, Aviva bioscience, Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences,
Chemicon International, Chemokine, Clontech, Cytolab, DAKO, Diagnostic BioSystems,
eBioscience, Endocrine Technologies, Enzo Biochem, Eurogentec, Fusion Antibodies, Genesis
Biotech, GloboZymes, Haematologic Technologies, Immunodetect, Immunodiagnostik,
Immunometrics, Immunostar, Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch
Laboratory, KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, Lee
Laboratories, Lifescreen, Maine Biotechnology Services, Mediclone, MicroPharm Ltd.,
ModiQuest, Molecular Innovations, Molecular Probes, Neoclone, Neuromics, New England
Biolabs, Novocastra, Novus Biologicals, Oncogene Research Products, Orbigen, Oxford
Biotechnology, Panvera, PerkinElmer Life Sciences, Pharmingen, Phoenix Pharmaceuticals,
Pierce Chemical Company, Polymun Scientific, Polysiences, Inc., Promega Corporation,
Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D Systems, Repligen, Research
Diagnostics, Roboscreen, Santa Cruz Biotechnology, Seikagaku America, Serological
Corporation, Serotec, SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH,
Technopharm, Terra Nova Biotechnology, TiterMax, Trillium Diagnostics, Upstate
Biotechnology, US Biological, Vector Laboratories, Wako Pure Chemical Industries, and
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Zeptometrix. However, the skilled artisan can routinely make antibodies, against any of the
polypeptide determinants described herein.
The presence of a label can be detected by inspection, or a detector which monitors a
particular probe or probe combination is used to detect the detection reagent label. Typical
detectors include spectrophotometers, phototubes and photodiodes, microscopes, scintillation
counters, cameras, film and the like, as well as combinations thereof. Those skilled in the art will
be familiar with numerous suitable detectors that widely available from a variety of commercial
sources and may be useful for carrying out the method disclosed herein. Commonly, an optical
image of a substrate comprising bound labeling moieties is digitized for subsequent computer
analysis. See generally The Immunoassay Handbook [The Immunoassay Handbook. Third
Edition. 2005].
Examples of antibodies for measuring TRAIL include without limitation: Mouse anti-
Human TRAIL Monoclonal antibody (RIK-2) (12-9927-42) (Invitrogen), Goat IgG anti-Human
TRAIL Polyclonal antibody (AF375) (R&D Systems), Mouse anti-Human TRAIL monoclonal
antibody [2E5] (ab2219) (Abcam), Mouse anti-Human Monoclonal antibody (Clone # 75402)
(MAB687) (R&D Systems),
Examples of antibodies for measuring CRP include without limitation: Rabbit anti-Human
C-Reactive Protein/CRP polyclonal antibody (ab31156) (Abcam), Sheep anti-Human C-Reactive
Protein/CRP Polyclonal antibody (AF1707) (R&D Systems), rabbit anti-Human C-Reactive
Protein/CRP Polyclonal antibody (C3527) (Sigma-Aldrich), Mouse anti-Human C-Reactive
Protein/CRP monoclonal antibody (C1688) (MilliporeSigma),
Examples of antibodies for measuring IP-10 include without limitation: Mouse anti-human
CXCL10 (IP-10) Monoclonal Antibody (Cat. No. 524401) (BioLegend), Rabbit anti-human
CXCL10 (IP-10) polyclonal Antibody (ab9807) (Abcam), Mouse anti-human CXCL10 (IP-10)
Monoclonal Antibody (4D5) (MCA1693) (Bio-Rad), Goat anti-human CXCL10 (IP-10)
Monoclonal Antibody (PA5-46999) (Invitrogen), Mouse anti-human CXCL10 (IP-10)
Monoclonal Antibody (MA5-23819) (Invitrogen),
Examples of antibodies for measuring IL-6 include without limitation: Mouse anti-human
IL-6 monoclonal antibody (Clone # 1936) (MAB2061) (R&D Systems), Mouse anti-human IL-6
monoclonal antibody (ab9324) (Abcam), Rat anti-human IL-6 monoclonal antibody (MQ2-39C3)
(501204) (BioLegend), Rabbit anti-human IL-6 monoclonal antibody (ab233706) (Abcam),
Examples of antibodies for measuring Procalcitonin (PCT) include without limitation:
Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (DMAB1342MH) (Creative
diagnostics), Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (MAB8350) (R&D
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Systems), Sheep Anti-Human Procalcitonin (PCT) polyclonal antibody (PA1-75362) (Invitrogen),
Mouse Anti-Human Procalcitonin (PCT) monoclonal antibody (6F10) (MA1-20888) (Invitrogen).
Examples of antibodies for measuring IL1R/IL1R1 include without limitation: Goat Anti-
Human IL1R1 Polyclonal antibody (PA5-46930) (Invitrogen), Mouse Anti-Human IL1R1
Monoclonal antibody (clone IL1 31-22.2.1) (MA1-10857) (Invitrogen), Goat Anti-Human IL1R1
Polyclonal antibody (AF269) (R&D Systems), Rabbit Anti-Human IL1R1 Polyclonal antibody
(ab106278) (Abcam), Mouse Anti-Human IL1R1 monoclonal antibody (sc-393998) (SANTA
CRUZ BIOTECHNOLOGY).
Examples of antibodies for measuring Serum Amyloid A1 (SAA/ SAA1) include without
limitation: Rabbit Anti-Human Serum Amyloid A1 (SAA/ SAA1) Polyclonal antibody (PA5-
112852) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1) Monoclonal
antibody (MA5-11729) (Invitrogen), Mouse Anti-Human Serum Amyloid A1 (SAA/ SAA1)
Monoclonal antibody (MBS592153) (MyBioSource), Mouse Anti-Human Serum Amyloid A1
(SAA/ SAA1) Monoclonal antibody (ab687) (Abcam), Mouse Anti-Human Serum Amyloid A1
(SAA/ SAA1) Monoclonal antibody (clone SAA19) (MCA6030GA) (BIO-RAD).
Examples of antibodies for measuring TREM1 include without limitation: Mouse Anti-
Human TREM-1 Monoclonal Antibody (R&D Systems), Rabbit Anti-Human TREM1 Polyclonal
Antibody (Merck), Rabbit Anti-Human TREM1 Polyclonal Antibody (Invitrogen), Mouse Anti-
Human CD354 (TREM-1) Monoclonal Antibody (BioLegend).
Examples of antibodies for measuring TREM2 include without limitation: Rat Anti-
Human TREM2 Monoclonal antibody (Clone # 237920) (MAB17291) (R&D Systems), Goat
Anti-Human TREM2 Polyclonal antibody (AF1828) (R&D Systems), Mouse Anti-Human
TREM2 Monoclonal antibody (clone 2B5) (NBP1-07101) (Novus Biologicals), Rabbit Anti-
Human TREM2 Monoclonal antibody (ab209814) (Abcam).
Examples of antibodies for measuring IL8 include without limitation: Mouse Anti-Human
IL-8 Monoclonal Antibody (ab18672) (Abcam), Mouse Anti-Human IL-8/CXCL8 Monoclonal
Antibody (R&D Systems), Mouse Anti-Human IL-8 (CXCL8) Monoclonal Antibody (Invitrogen).
Examples of antibodies for measuring IL-15 include without limitation: Mouse Anti-
Human IL-15 Monoclonal Antibody (MA5-23729) (Invitrogen), Mouse Anti-Human IL-15
Monoclonal Antibody (16-0157-82) (Invitrogen), Rabbit Anti-Human IL-15 Polyclonal Antibody
(PA5-102871) (Invitrogen), Mouse Anti-Human IL-15 Monoclonal Antibody (ab55276) (Abcam),
Mouse Anti-Human IL-15 Monoclonal Antibody (Clone # 34559) (MAB2471) (R&D Systems).
Examples of antibodies for measuring IL-12 include without limitation: Goat Anti-Human
IL-12 Polyclonal Antibody (AF-219-NA) (R&D Systems), Mouse Anti-Human IL-12 Monoclonal
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Antibody (Clone # 24910) (MAB219) (R&D Systems), Goat Anti-Human IL-12 Polyclonal
Antibody (ab9992) (Abcam), Rat Anti-Human IL-12 Monoclonal Antibody (16-8126-85)
(Invitrogen).
Examples of antibodies for measuring IL-10 include without limitation: Mouse Anti-
Human IL-10 Monoclonal Antibody (Clone # 127107) (MAB2172) (R&D Systems), Rat Anti-
Human IL-10 Monoclonal Antibody (clone JES3-9D7) (501403) (BioLegend), Rabbit Anti-
Human IL-10 Polyclonal Antibody (ab34843) (Abcam), Rat Anti-Human IL-10 Monoclonal
Antibody (clone JES3-12G8) (MA1-82664) (Invitrogen).
Examples of antibodies for measuring MCP-1 include without limitation: Rabbit anti-
Human MCP-1 Polyclonal antibody (ab9669) (Abcam), Mouse anti-Human MCP-1 Monoclonal
antibody (Clone # 23007) (MAB679) (R&D Systems), Mouse anti-Human MCP-1 Monoclonal
antibody (Clone 2D8) (MA5-17040) (Invitrogen), Mouse anti-Human MCP-1 Monoclonal
antibody (Clone 5D3-F7) (MCA5981GA) (BIO-RAD).
Examples of antibodies for measuring IL-2R (IL-2RA) include without limitation: Mouse
anti-Human IL-2R (IL-2RA) Monoclonal antibody (ab9496) (Abcam), Mouse anti-Human IL-2R
(IL-2RA) Monoclonal antibody (Clone # 24212) (MAB1020) (R&D Systems), Mouse anti-Human
IL-2R (IL-2RA) Monoclonal antibody (Clone YNRhIL2R) (ANT-104) (ProSpec).
Examples of antibodies for measuring GDF15 include without limitation: Goat anti-
Human GDF15 Polyclonal antibody (AF957) (R&D Systems), Mouse anti-Human GDF15
Monoclonal antibody (Clone # 147627) (MAB957) (R&D Systems), Goat anti-Human GDF15
Polyclonal antibody (ab39999) (Abcam), Rabit anti-Human GDF15 Polyclonal antibody
(ab106006) (Abcam), Rabit anti-Human GDF15 Polyclonal antibody (HPA011191) (Sigma-
Aldrich).
Examples of antibodies for measuring MBL include without limitation: Goat anti-Human
MBL Polyclonal antibody (AF2307) (R&D Systems), Mouse anti-Human MBL Monoclonal
antibody (3B6) (ab23457) (Abcam), Goat anti-Human MBL Polyclonal antibody (AF2307)
(Novus Biologicals).
Examples of antibodies for measuring CD27 include without limitation: Goat anti-Human
CD27 Polyclonal antibody (AF382) (R&D Systems), Mouse anti-Human CD27 monoclonal
antibody (clone O323) (47-0279-42) (Invitrogen), Rabbit anti-Human CD27 Polyclonal antibody
(PA5-83443) (Invitrogen), Mouse anti-Human CD27 monoclonal antibody (clone CLB-27/1)
(MHCD2704) (Invitrogen).
Examples of antibodies for measuring MMP2 include without limitation: Rabbit anti-
Human MMP2 Polyclonal antibody (ab97779) (Abcam), Mouse anti-Human MMP2 monoclonal
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antibody (6E3F8) (ab86607) (Abcam), Mouse anti-Human MMP2 monoclonal antibody (Clone #
36006) (MAB902) (R&D Systems), Goat anti-Human MMP2 polyclonal antibody (AF902) (R&D
Systems), Rabbit anti-Human MMP2 Polyclonal antibody (AHP2735) (BIO-RAD).
Examples of antibodies for measuring RESISTIN include without limitation: Goat anti-
Human RESISTIN Polyclonal antibody (AF1359) (R&D Systems), Mouse anti-Human
RESISTIN Monoclonal antibody (Clone # 184305) (MAB13591) (R&D Systems), Rabbit anti-
Human RESISTIN Monoclonal antibody (clone EP4738) (ab124681) (Abcam), Mouse anti-
Human RESISTIN Monoclonal antibody (sc-376336) (SANTA CRUZ BIOTECHNOLOGY).
Examples of antibodies for measuring RSAD2 include without limitation: Rabbit anti-
Human RSAD2 Polyclonal antibody (HPA041160) (Sigma-Aldrich), Mouse anti-Human RSAD2
Monoclonal antibody (sc-390342) (SANTA CRUZ BIOTECHNOLOGY), Mouse anti-Human
RSAD2 Monoclonal antibody (OTI4D12) (TA505799) (OriGene), Rabbit anti-Human RSAD2
Polyclonal antibody (LS-C378833) (LSBio), Rabbit anti-Human RSAD2 Polyclonal antibody
(TA329507) (OriGene).
Examples of antibodies for measuring MX1 include without limitation: Rabbit anti-Human
MX1 Polyclonal antibody (ab95926) (Abcam), Goat anti-Human MX1 Polyclonal antibody
(AF7946) (R&D Systems), Mouse anti-Human MX1 monoclonal antibody (sc-271024) (SANTA
CRUZ BIOTECHNOLOGY), Rabbit anti-Human MX1 Polyclonal antibody (PA5-56590)
(Invitrogen), Mouse anti-Human MX1 Monoclonal antibody (OTI2G12) (MA5-24914)
(Invitrogen), Mouse anti-Human MX1 Monoclonal antibody (GT4812) (MA5-31483)
(Invitrogen).
Examples of antibodies for measuring TIE2 include without limitation: Mouse anti-Human
TIE2 Monoclonal antibody (Cl. 16) (ab24859) (Abcam), Goat anti-Human TIE2 Polyclonal
antibody (AF313) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (Clone #
83715) (MAB3131) (R&D Systems), Mouse anti-Human TIE2 Monoclonal antibody (clone
33.1 (Ab33)) (334205) (BioLegend), Rabbit anti-Human TIE2 Polyclonal antibody
(SAB4502942) (Sigma-Aldrich).
Examples of antibodies for measuring VCAM-1/CD106 include without limitation: Mouse
anti-Human VCAM-1 Monoclonal antibody (Clone # BBIG-V1) (BBA5) (R&D Systems), Rabbit
anti-Human VCAM-1 Monoclonal antibody (EPR5047) (ab134047) (Abcam), Mouse anti-Human
VCAM-1 Monoclonal antibody (clone 1.4C3) (MA5-11447) (Invitrogen).
Examples of antibodies for measuring CD14 include without limitation: Mouse anti-
Human CD14 Monoclonal antibody (4B4F12) (ab182032) (Abcam), Mouse anti-Human CD14
Monoclonal antibody (M5E2) (301805) (BioLegend), Mouse anti-Human CD14 Monoclonal
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antibody (Clone # 134620) (MAB3832) (R&D Systems), Mouse anti-Human CD14 Monoclonal
antibody (clone TÜK4) (MCA1568) (Bio-Rad).
Examples of antibodies for measuring IGFBP-3 include without limitation: Mouse anti-
Human IGFBP-3 Monoclonal antibody (Clone # 84728) (MAB305) (R&D Systems), Goat anti-
Human IGFBP-3 Polyclonal antibody (AF675) (R&D Systems), Goat anti-Human IGFBP-3
Polyclonal antibody (ab77635) (Abcam), Rabbit anti-Human IGFBP-3 Polyclonal antibody (PA5-
29711) (Invitrogen).
Examples of antibodies for measuring APRIL include without limitation: Mouse anti-
Human APRIL/ TNFSF13 Monoclonal antibody (JE49-07) (MA5-34866) (Invitrogen), Mouse
anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670820) (MAB5860) (R&D
Systems), Mouse anti-Human APRIL/ TNFSF13 Monoclonal antibody (Clone # 670840)
(MAB8843) (R&D Systems), Rabbit anti-Human APRIL/ TNFSF13 Polyclonal antibody
(ab3681) (Abcam).
Examples of antibodies for measuring Adiponectin include without limitation: Mouse anti-
Human Adiponectin Monoclonal antibody (19F1) (ab22554) (Abcam), Rabbit anti-Human
Adiponectin Polyclonal antibody (ab25891) (Abcam), Mouse anti-Human Adiponectin
Monoclonal antibody (Clone # 553517) (MAB10652) (R&D Systems), Goat anti-Human
Adiponectin Polyclonal antibody (AF1065) (R&D Systems), Rabbit anti-Human Adiponectin
Polyclonal antibody (A6354) (Sigma-Aldrich).
Examples of antibodies for measuring Angiogenin include without limitation: Goat anti-
Human Angiogenin Polyclonal antibody (AF265) (R&D Systems), Goat anti-Human Angiogenin
Polyclonal antibody (AB-265) (R&D Systems), Rabbit anti-Human Angiogenin Polyclonal
antibody (ab189207) (Abcam), Mouse anti-Human Angiogenin Monoclonal antibody (clone
MANG-1) (0555-5008) (Bio-Rad).
Examples of antibodies for measuring Angiopoietin 2/ANG2 include without limitation:
Rabbit anti-Human Angiopoietin 2/ANG2 Polyclonal antibody (ab153934) (Abcam), Goat anti-
Human Angiopoietin 2/ANG2 Polyclonal antibody (AF623) (R&D Systems), Mouse anti-Human
Angiopoietin 2/ANG2 Monoclonal antibody (Clone # 180102) (MAB0983) (R&D Systems),
Mouse anti-Human Angiopoietin 2/ANG2 Monoclonal antibody (Clone # M5203F01) (682702)
(BioLegend).
Examples of antibodies for measuring CLUSTERIN include without limitation: Rabbit
anti-Human CLUSTERIN Polyclonal antibody (ab69644) (Abcam), Rabbit anti-Human
CLUSTERIN Monoclonal antibody [EPR2911] (ab92548) (Abcam), Mouse anti-Human
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CLUSTERIN Monoclonal antibody (Clone # 350227) (MAB2937) (R&D Systems), Mouse anti-
Human CLUSTERIN Monoclonal antibody (Clone # 350270) (MAB29372) (R&D Systems).
Examples of antibodies for measuring CD95 include without limitation: Mouse anti-
Human CD95 Monoclonal antibody (clone DX2) (BioLegend), Mouse anti-Human CD95
Monoclonal antibody (clone EOS9.1) (BioLegend), Mouse anti-Human CD95 Monoclonal
antibody (clone LOB 3/17) (Bio-Rad).
Examples of antibodies for measuring uPAR include without limitation: Mouse anti-
Human uPAR Monoclonal antibody (Clone # 62022) (MAB807) (R&D Systems), Goat anti-
Human uPAR Polyclonal antibody (AF807) (R&D Systems), Rabbit anti-Human uPAR
Monoclonal antibody (clone 2G10) (MABC88) (Sigma-Aldrich).
Examples of antibodies for measuring IL7R include without limitation: Mouse anti-Human
IL7R /CD127 Monoclonal antibody (Clone # 40131) (MAB306) (R&D Systems), Mouse anti-
Human IL7R /CD127 Monoclonal antibody (Clone A019D5) (351303) (BioLegend), Mouse anti-
Human IL7R /CD127 Monoclonal antibody (eBioRDR5) (48-1278-42) (Invitrogen), Rabbit anti-
Human IL7R /CD127 Polyclonal antibody (PA5-97870) (Invitrogen).
Examples of antibodies for measuring PTEN include without limitation: Mouse anti-
Human PTEN Monoclonal antibody (Clone # 217702) (MAB847) (R&D Systems), Rabbit anti-
Human PTEN Polyclonal antibody (ab31392) (Abcam), Mouse anti-Human PTEN Monoclonal
antibody (A2b1) (ab79156) (Abcam), Mouse anti-Human PTEN Monoclonal antibody (clone
6H2.1) (Sigma-Aldrich).
Examples of antibodies for measuring MMP8 include without limitation: Mouse anti-
Human MMP8 Monoclonal antibody (Clone # 100608) (MAB9081) (R&D Systems), Mouse anti-
Human MMP8 Monoclonal antibody (Clone # 100619) (MAB908) (R&D Systems), Rabbit anti-
Human MMP8 Monoclonal antibody (EP1252Y) (ab81286) (Abcam), Rabbit anti-Human MMP8
Polyclonal antibody (PA5-28246) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody
(PA5-82805) (Invitrogen), Rabbit anti-Human MMP8 Polyclonal antibody (HPA021221) (Sigma-
Aldrich).
Examples of antibodies for measuring Ferritin include without limitation: Mouse anti-
Human Ferritin Monoclonal antibody (Clone # 962609) (MAB93541) (R&D Systems), Sheep
anti-Human Ferritin Polyclonal antibody (AHP2179G) (Bio-Rad), Mouse anti-Human Ferritin
Monoclonal antibody (clone F23 (7A4)) (4420-3010) (Bio-Rad), Goat anti-Human Ferritin
Polyclonal antibody (PA5-19058) (Invitrogen), Mouse anti-Human Ferritin Monoclonal antibody
(clone 101) (MIF2501) (Invitrogen), Rabbit anti-Human Ferritin Monoclonal antibody
(EPR3004Y) (ab75973) (Abcam).
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Examples of antibodies for measuring D-Dimer include without limitation: Mouse anti-
Human D-Dimer Monoclonal antibody (clone DD1) (MCA2523) (Bio-Rad), Mouse anti-Human
D-Dimer Monoclonal antibody (3B6) (ab273889) (Abcam), Rabbit anti-Human D-Dimer
Monoclonal antibody (Clone # 2609D) (MAB104712) (R&D Systems), Mouse anti-Human D-
Dimer Monoclonal antibody (clone DD2) (NB110-8376) (Novus).
Soluble TRAIL and membrane TRAIL can be distinguished by using different measuring
techniques and samples. For example, Soluble TRAIL can be measured without limitation in cell
free samples such as serum or plasma, using without limitation lateral flow immunoassay (LFIA),
as further described herein below. Membrane TRAIL can be measured in samples that contain
cells using cell based assays including without limitation flow cytometry, ELISA, and other
immunoassays.
Lateral Flow Immunoassays (LFIA): This is a technology which allows rapid
measurement of analytes at the point of care (POC) and its underlying principles are described
below. According to one embodiment, LFIA is used in the context of a hand-held device.
The technology is based on a series of capillary beds, such as pieces of porous paper or
sintered polymer. Each of these elements has the capacity to transport fluid (e.g., urine)
spontaneously. The first element (the sample pad) acts as a sponge and holds an excess of sample
fluid. Once soaked, the fluid migrates to the second element (conjugate pad) in which the
manufacturer has stored the so-called conjugate, a dried format of bio-active particles (see below)
in a salt-sugar matrix that contains everything to guarantee an optimized chemical reaction
between the target molecule (e.g., an antigen) and its chemical partner (e.g., antibody) that has
been immobilized on the particle's surface. While the sample fluid dissolves the salt-sugar matrix,
it also dissolves the particles and in one combined transport action the sample and conjugate mix
while flowing through the porous structure. In this way, the analyte binds to the particles while
migrating further through the third capillary bed. This material has one or more areas (often called
stripes) where a third molecule has been immobilized by the manufacturer. By the time the sample-
conjugate mix reaches these strips, analyte has been bound on the particle and the third 'capture'
molecule binds the complex.
After a while, when more and more fluid has passed the stripes, particles accumulate and
the stripe-area changes color. Typically there are at least two stripes: one (the control) that captures
any particle and thereby shows that reaction conditions and technology worked fine, the second
contains a specific capture molecule and only captures those particles onto which an analyte
molecule has been immobilized. After passing these reaction zones the fluid enters the final porous
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material, the wick, that simply acts as a waste container. Lateral Flow Tests can operate as either
competitive or sandwich assays.
Different formats may be adopted in LFIA. Strips used for LFIA contain four main
components. A brief description of each is given before describing format types.
Sample application pad: It is made of cellulose and/or glass fiber and sample is applied on
this pad to start assay. Its function is to transport the sample to other components of lateral flow
test strip (LFTS). Sample pad should be capable of transportation of the sample in a smooth,
continuous and homogenous manner. Sample application pads are sometimes designed to pretreat
the sample before its transportation. This pretreatment may include separation of sample
components, removal of interferences, adjustment of pH, etc.
Conjugate pad: It is the place where labeled biorecognition molecules are dispensed.
Material of conjugate pad should immediately release labeled conjugate upon contact with moving
liquid sample. Labeled conjugate should stay stable over entire life span of lateral flow strip. Any
variations in dispensing, drying or release of conjugate can change results of assay significantly.
Poor preparation of labeled conjugate can adversely affect sensitivity of assay. Glass fiber,
cellulose, polyesters and some other materials are used to make conjugate pad for LFIA. Nature
of conjugate pad material has an effect on release of labeled conjugate and sensitivity of assay.
Nitrocellulose membrane: It is highly critical in determining sensitivity of LFIA.
Nitrocellulose membranes are available in different grades. Test and control lines are drawn over
this piece of membrane. So an ideal membrane should provide support and good binding to capture
probes (antibodies, aptamers etc.). Nonspecific adsorption over test and control lines may affect
results of assay significantly, thus a good membrane will be characterized by lesser non-specific
adsorption in the regions of test and control lines. Wicking rate of nitrocellulose membrane can
influence assay sensitivity. These membranes are easy to use, inexpensive, and offer high affinity
for proteins and other biomolecules. Proper dispensing of bioreagents, drying and blocking play a
role in improving sensitivity of assay.
Adsorbent pad: It works as sink at the end of the strip. It also helps in maintaining flow
rate of the liquid over the membrane and stops back flow of the sample. Adsorbent capacity to
hold liquid can play an important role in results of assay.
All these components are fixed or mounted over a backing card. Materials for backing card
are highly flexible because they have nothing to do with LFIA except providing a platform for
proper assembling of all the components. Thus backing card serves as a support and it makes easy
to handle the strip.
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Major steps in LFIA are (i) preparation of antibody against target analyte (ii) preparation
of label (iii) labeling of biorecognition molecules (iv) assembling of all components onto a backing
card after dispensing of reagents at their proper pads (v) application of sample and obtaining
results.
Sandwich format: In a typical format, label (Enzymes or nanoparticles or fluorescence
dyes) coated antibody or aptamer is immobilized at conjugate pad. This is a temporary adsorption
which can be flushed away by flow of any buffer solution. A primary antibody or aptamer against
target analyte is immobilized over test line. A secondary antibody or probe against labeled
conjugate antibody/aptamer is immobilized at control zone.
Sample containing the analyte is applied to the sample application pad and it subsequently
migrates to the other parts of strip. At conjugate pad, target analyte is captured by the immobilized
labeled antibody or aptamer conjugate and results in the formation of labeled antibody
conjugate/analyte complex. This complex now reaches at nitrocellulose membrane and moves
under capillary action. At test line, label antibody conjugate/analyte complex is captured by
another antibody which is primary to the analyte. Analyte becomes sandwiched between labeled
and primary antibodies forming labeled antibody conjugate/analyte/primary antibody complex.
Excess labeled antibody conjugate will be captured at control zone by secondary antibody. Buffer
or excess solution goes to absorption pad. Intensity of color at test line corresponds to the amount
of target analyte and is measured with an optical strip reader or visually inspected. Appearance of
color at control line ensures that a strip is functioning properly.
Competitive format: Such a format suits best for low molecular weight compounds which
cannot bind two antibodies simultaneously. Absence of color at test line is an indication for the
presence of analyte while appearance of color both at test and control lines indicates a negative
result. Competitive format has two layouts. In the first layout, solution containing target analyte is
applied onto the sample application pad and prefixed labeled biomolecule (antibody/aptamer)
conjugate gets hydrated and starts flowing with moving liquid. Test line contains pre-immobilized
antigen (same analyte to be detected) which binds specifically to label conjugate. Control line
contains pre-immobilized secondary antibody which has the ability to bind with labeled antibody
conjugate. When liquid sample reaches at the test line, pre-immobilized antigen will bind to the
labeled conjugate in case target analyte in sample solution is absent or present in such a low
quantity that some sites of labeled antibody conjugate were vacant. Antigen in the sample solution
and the one which is immobilized at test line of strip compete to bind with labeled conjugate. In
another layout, labeled analyte conjugate is dispensed at conjugate pad while a primary antibody
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to analyte is dispensed at test line. After application of analyte solution a competition takes place
between analyte and labeled analyte to bind with primary antibody at test line.
Multiplex detection format: Multiplex detection format is used for detection of more than
one target species and assay is performed over the strip containing test lines equal to number of
target species to be analyzed. It is highly desirable to analyze multiple analytes simultaneously
under same set of conditions. Multiplex detection format is very useful in clinical diagnosis where
multiple analytes which are inter-dependent in deciding about the stage of a disease are to be
detected. Lateral flow strips for this purpose can be built in various ways i.e. by increasing length
and test lines on conventional strip, making other structures like stars or T-shapes. Shape of strip
for LFIA will be dictated by number of target analytes. Miniaturized versions of LFIA based on
microarrays for multiplex detection of DNA sequences have been reported to have several
advantages such as less consumption of test reagents, requirement of lesser sample volume and
better sensitivity.
Labels: Any material that is used as a label should be detectable at very low concentrations
and it should retain its properties upon conjugation with biorecognition molecules. This
conjugation is also expected not to change features of biorecognition probes. Ease in conjugation
with biomolecules and stability over longer period of time are desirable features for a good label.
–9
Concentrations of labels down to 10 M are optically detectable. After the completion of assay,
some labels generate direct signal (as color from gold colloidal) while others require additional
steps to produce analytical signal (as enzymes produce detectable product upon reaction with
suitable substrate). Hence the labels which give direct signal are preferable in LFA because of less
time consumption and reduced procedure.
Gold nanoparticles: Colloidal gold nanoparticles are the most commonly used labels in
LFA. Colloidal gold is inert and gives very perfect spherical particles. These particles have very
high affinity toward biomolecules and can be easily functionalized. Optical properties of gold
nanoparticles are dependent on size and shape. Size of particles can be tuned by use of suitable
chemical additives. Their unique features include environment friendly preparation, high affinity
toward proteins and biomolecules, enhanced stability, exceptionally higher values for charge
transfer and good optical signaling. Optical signal of gold nanoparticles in colorimetric LFA can
be amplified by deposition of silver, gold nanoparticles and enzymes.
Magnetic particles and aggregates: Colored magnetic particles produce color at the test
line which is measured by an optical strip reader but magnetic signals coming from magnetic
particles can also be used as detection signals and recorded by a magnetic assay reader. Magnetic
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signals are stable for longer time compared to optical signals and they enhance sensitivity of LFA
by 10 to 1000 folds.
Fluorescent and luminescent materials: Fluorescent molecules are widely used in LFA
as labels and the amount of fluorescence is used to quantitate the concentration of analyte in the
sample. Detection of proteins is accomplished by using organic fluorophores such as rhodamine
as labels in LFA.
Current developments in nanomaterial have headed to manufacture of quantum dots which
display very unique electrical and optical properties. These semiconducting particles are not only
water soluble but can also be easily combined with biomolecules because of closeness in
dimensions. Owing to their unique optical properties, quantum dots have come up as a substitute
to organic fluorescent dyes. Like gold nanoparticles QDs show size dependent optical properties
and a broad spectrum of wavelengths can be monitored. Single light source is sufficient to excite
quantum dots of all different sizes. QDs have high photo stability and absorption coefficients.
Upconverting phosphors (UCP) are characterized by their excitation in infra-red region and
emission in high energy visible region. Compared to other fluorescent materials, they have a
unique advantage of not showing any auto fluorescence. Because of their excitation in IR regions,
they do not photo degrade biomolecules. A major advantage lies in their production from easily
available bulk materials. Although difference in batch to batch preparation of UCP reporters can
affect sensitivity of analysis in LFA, it was observed that they can enhance sensitivity of analytical
signal by 10 to 100 folds compared to gold nanoparticles or colored latex beads, when analysis is
carried out under same set of biological conditions.
Enzymes: Enzymes are also employed as labels in LFA. But they increase one step in LFA
which is application of suitable substrate after complete assay. This substrate will produce color
at test and control lines as a result of enzymatic reaction. In case of enzymes, selection of suitable
enzyme substrate combination is one necessary requirement in order to get a colored product for
strip reader or electroactive product for electrochemical detection. In other words, sensitivity of
detection is dependent on enzyme substrate combination.
Colloidal carbon: Colloidal carbon is comparatively inexpensive label and its production
can be easily scaled up. Because of their black color, carbon NPs can be easily detected with high
sensitivity. Colloidal carbon can be functionalized with a large variety of biomolecules for
detection of low and high molecular weight analytes.
Detection systems: In case of gold nanoparticles or other color producing labels, qualitative
or semi-quantitative analysis can be done by visual inspection of colors at test and control lines.
The major advantage of visual inspection is rapid qualitative answer in “Yes” or “NO”. Such quick
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replies about presence of an analyte in clinical analysis have very high importance. Such tests help
doctors to make an immediate decision near the patients in hospitals in situations where test results
from central labs cannot be waited for because of huge time consumption. But for quantification,
optical strip readers are employed for measurement of the intensity of colors produced at test and
control lines of strip. This is achieved by inserting the strips into a strip reader and intensities are
recorded simultaneously by imaging softwares. Optical images of the strips can also be recorded
with a camera and then processed by using a suitable software. Procedure includes proper
placement of strip under the camera and a controlled amount of light is thrown on the areas to be
observed. Such systems use monochromatic light and wavelength of light can be adjusted to get a
good contrast among test and control lines and background. In order to provide good quantitative
and reproducible results, detection system should be sensitive to different intensities of colors.
Optical standards can be used to calibrate an optical reader device. Automated systems have
advantages over manual imaging and processing in terms of time consumption, interpretation of
results and adjustment of variables.
In case of fluorescent labels, a fluorescence strip reader is used to record fluorescence
intensity of test and control lines. Fluorescence brightness of test line increased with an increase
in nitrated seruloplasmin concentration in human serum when it was detected with a fluorescence
strip reader. A photoelectric sensor was also used for detection in LFIA where colloidal gold is
exposed to light emitting diode and resulting photoelectrons are recorded. Chemiluminescence
which results from reaction of enzyme and substrate is measured as a response to amount of target
analyte. Magnetic strip readers and electrochemical detectors are also reported as detection
systems in LFTS but they are not very common. Selection of detector is mainly determined by the
label employed in analysis.
Methods of quantitating levels of RNA markers are known in the art and are summarized
infra:
Northern Blot analysis: This method involves the detection of a particular RNA in a
mixture of RNAs. An RNA sample is denatured by treatment with an agent (e.g., formaldehyde)
that prevents hydrogen bonding between base pairs, ensuring that all the RNA molecules have an
unfolded, linear conformation. The individual RNA molecules are then separated according to size
by gel electrophoresis and transferred to a nitrocellulose or a nylon-based membrane to which the
denatured RNAs adhere. The membrane is then exposed to labeled DNA probes. Probes may be
labeled using radio-isotopes or enzyme linked nucleotides. Detection may be using
autoradiography, colorimetric reaction or chemiluminescence. This method allows both
quantitation of an amount of particular RNA molecules and determination of its identity by a
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relative position on the membrane which is indicative of a migration distance in the gel during
electrophoresis.
RT-PCR analysis: This method uses PCR amplification of relatively rare RNAs molecules.
First, RNA molecules are purified from the cells and converted into complementary DNA (cDNA)
using a reverse transcriptase enzyme (such as an MMLV-RT) and primers such as, oligo dT,
random hexamers or gene specific primers. Then by applying gene specific primers and Taq DNA
polymerase, a PCR amplification reaction is carried out in a PCR machine. Those of skills in the
art are capable of selecting the length and sequence of the gene specific primers and the PCR
conditions (i.e., annealing temperatures, number of cycles and the like) which are suitable for
detecting specific RNA molecules. It will be appreciated that a semi-quantitative RT-PCR reaction
can be employed by adjusting the number of PCR cycles and comparing the amplification product
to known controls.
RNA in situ hybridization stain: In this method DNA or RNA probes are attached to the
RNA molecules present in the cells. Generally, the cells are first fixed to microscopic slides to
preserve the cellular structure and to prevent the RNA molecules from being degraded and then
are subjected to hybridization buffer containing the labeled probe. The hybridization buffer
includes reagents such as formamide and salts (e.g., sodium chloride and sodium citrate) which
enable specific hybridization of the DNA or RNA probes with their target mRNA molecules in
situ while avoiding non-specific binding of probe. Those of skills in the art are capable of adjusting
the hybridization conditions (i.e., temperature, concentration of salts and formamide and the like)
to specific probes and types of cells. Following hybridization, any unbound probe is washed off
and the bound probe is detected using known methods. For example, if a radio-labeled probe is
used, then the slide is subjected to a photographic emulsion which reveals signals generated using
radio-labeled probes; if the probe was labeled with an enzyme then the enzyme-specific substrate
is added for the formation of a colorimetric reaction; if the probe is labeled using a fluorescent
label, then the bound probe is revealed using a fluorescent microscope; if the probe is labeled using
a tag (e.g., digoxigenin, biotin, and the like) then the bound probe can be detected following
interaction with a tag-specific antibody which can be detected using known methods.
In situ RT-PCR stain: This method is described in Nuovo GJ, et al. [Intracellular
localization of polymerase chain reaction (PCR)-amplified hepatitis C cDNA. Am J Surg Pathol.
1993, 17: 683-90] and Komminoth P, et al. [Evaluation of methods for hepatitis C virus detection
in archival liver biopsies. Comparison of histology, immunohistochemistry, in situ hybridization,
reverse transcriptase polymerase chain reaction (RT-PCR) and in situ RT-PCR. Pathol Res Pract.
1994, 190: 1017-25]. Briefly, the RT-PCR reaction is performed on fixed cells by incorporating
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labeled nucleotides to the PCR reaction. The reaction is carried on using a specific in situ RT-PCR
apparatus such as the laser-capture microdissection PixCell I LCM system available from Arcturus
Engineering (Mountainview, CA).
DNA microarrays/DNA chips:
The expression of thousands of genes may be analyzed simultaneously using DNA
microarrays, allowing analysis of the complete transcriptional program of an organism during
specific developmental processes or physiological responses. DNA microarrays consist of
thousands of individual gene sequences attached to closely packed areas on the surface of a support
such as a glass microscope slide. Various methods have been developed for preparing DNA
microarrays. In one method, an approximately 1 kilobase segment of the coding region of each
gene for analysis is individually PCR amplified. A robotic apparatus is employed to apply each
amplified DNA sample to closely spaced zones on the surface of a glass microscope slide, which
is subsequently processed by thermal and chemical treatment to bind the DNA sequences to the
surface of the support and denature them. Typically, such arrays are about 2 x 2 cm and contain
about individual nucleic acids 6000 spots. In a variant of the technique, multiple DNA
oligonucleotides, usually 20 nucleotides in length, are synthesized from an initial nucleotide that
is covalently bound to the surface of a support, such that tens of thousands of identical
oligonucleotides are synthesized in a small square zone on the surface of the support. Multiple
oligonucleotide sequences from a single gene are synthesized in neighboring regions of the slide
for analysis of expression of that gene. Hence, thousands of genes can be represented on one glass
slide. Such arrays of synthetic oligonucleotides may be referred to in the art as “DNA chips”, as
opposed to “DNA microarrays”, as described above [Lodish et al. (eds.). Chapter 7.8: DNA
Microarrays: Analyzing Genome-Wide Expression. In: Molecular Cell Biology, 4th ed., W. H.
Freeman, New York. (2000)].
Oligonucleotide microarray – In this method oligonucleotide probes capable of
specifically hybridizing with the polynucleotides of some embodiments of the invention are
attached to a solid surface (e.g., a glass wafer). Each oligonucleotide probe is of approximately
-25 nucleic acids in length. To detect the expression pattern of the polynucleotides of some
embodiments of the invention in a specific cell sample (e.g., blood cells), RNA is extracted from
the cell sample using methods known in the art (using e.g., a TRIZOL solution, Gibco BRL, USA).
Hybridization can take place using either labeled oligonucleotide probes (e.g., 5'-biotinylated
probes) or labeled fragments of complementary DNA (cDNA) or RNA (cRNA). Briefly, double
stranded cDNA is prepared from the RNA using reverse transcriptase (RT) (e.g., Superscript II
RT), DNA ligase and DNA polymerase I, all according to manufacturer’s instructions (Invitrogen
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Life Technologies, Frederick, MD, USA). To prepare labeled cRNA, the double stranded cDNA
is subjected to an in vitro transcription reaction in the presence of biotinylated nucleotides using
e.g., the BioArray High Yield RNA Transcript Labeling Kit (Enzo, Diagnostics, Affymetix Santa
Clara CA). For efficient hybridization the labeled cRNA can be fragmented by incubating the
RNA in 40 mM Tris Acetate (pH 8.1), 100 mM potassium acetate and 30 mM magnesium acetate
for 35 minutes at 94 C. Following hybridization, the microarray is washed and the hybridization
signal is scanned using a confocal laser fluorescence scanner which measures fluorescence
intensity emitted by the labeled cRNA bound to the probe arrays.
For example, in the Affymetrix microarray (Affymetrix®, Santa Clara, CA) each gene on
the array is represented by a series of different oligonucleotide probes, of which, each probe pair
consists of a perfect match oligonucleotide and a mismatch oligonucleotide. While the perfect
match probe has a sequence exactly complimentary to the particular gene, thus enabling the
measurement of the level of expression of the particular gene, the mismatch probe differs from the
perfect match probe by a single base substitution at the center base position. The hybridization
signal is scanned using the Agilent scanner, and the Microarray Suite software subtracts the non-
specific signal resulting from the mismatch probe from the signal resulting from the perfect match
probe.
RNA sequencing: Methods for RNA sequence determination are generally known to the
person skilled in the art. Preferred sequencing methods are next generation sequencing methods
or parallel high throughput sequencing methods. An example of an envisaged sequence method is
pyrosequencing, in particular 454 pyrosequencing, e.g. based on the Roche 454 Genome
Sequencer. This method amplifies DNA inside water droplets in an oil solution with each droplet
containing a single DNA template attached to a single primer-coated bead that then forms a clonal
colony. Pyrosequencing uses luciferase to generate light for detection of the individual nucleotides
added to the nascent DNA, and the combined data are used to generate sequence read-outs. Yet
another envisaged example is Illumina or Solexa sequencing, e.g. by using the Illumina Genome
Analyzer technology, which is based on reversible dye-terminators. DNA molecules are typically
attached to primers on a slide and amplified so that local clonal colonies are formed. Subsequently
one type of nucleotide at a time may be added, and non-incorporated nucleotides are washed away.
Subsequently, images of the fluorescently labeled nucleotides may be taken and the dye is
chemically removed from the DNA, allowing a next cycle. Yet another example is the use of
Applied Biosystems' SOLiD technology, which employs sequencing by ligation. This method is
based on the use of a pool of all possible oligonucleotides of a fixed length, which are labeled
according to the sequenced position. Such oligonucleotides are annealed and ligated.
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Subsequently, the preferential ligation by DNA ligase for matching sequences typically results in
a signal informative of the nucleotide at that position. Since the DNA is typically amplified by
emulsion PCR, the resulting bead, each containing only copies of the same DNA molecule, can be
deposited on a glass slide resulting in sequences of quantities and lengths comparable to Illumina
sequencing. A further method is based on Helicos' Heliscope technology, wherein fragments are
captured by polyT oligomers tethered to an array. At each sequencing cycle, polymerase and single
fluorescently labeled nucleotides are added and the array is imaged. The fluorescent tag is
subsequently removed and the cycle is repeated. Further examples of sequencing techniques
encompassed within the methods of the present invention are sequencing by hybridization,
sequencing by use of nanopores, microscopy-based sequencing techniques, microfluidic Sanger
sequencing, or microchip-based sequencing methods. The present invention also envisages further
developments of these techniques, e.g. further improvements of the accuracy of the sequence
determination, or the time needed for the determination of the genomic sequence of an organism
etc.
According to one embodiment, the sequencing method comprises deep sequencing.
As used herein, the term “deep sequencing” refers to a sequencing method wherein the
target sequence is read multiple times in the single test. A single deep sequencing run is composed
of a multitude of sequencing reactions run on the same target sequence and each, generating
independent sequence readout.
It will be appreciated that in order to analyze the amount of an RNA, oligonucleotides may
be used that are capable of hybridizing thereto or to cDNA generated therefrom. According to one
embodiment a single oligonucleotide is used to determine the presence of a particular determinant,
at least two oligonucleotides are used to determine the presence of a particular determinant, at least
five oligonucleotides are used to determine the presence of a particular determinant, at least four
oligonucleotides are used to determine the presence of a particular determinant, at least five or
more oligonucleotides are used to determine the presence of a particular determinant.
Oligonucleotides that are capable of specifically hybridizing to the determinants described
herein may comprise a detectable moiety (e.g. a fluorescent moiety, a phosphorescent moiety)
and/or a quencher moiety.
According to embodiments of the present invention, the fluorescent moiety is selected from
the group consisting of an Atto dye, fluorescein, fluorescein chlorotriazinyl, rhodamine green,
rhodamine red, tetramethylrhodamine, FITC, Oregon green, Alexa Fluor, FAM, JOE, ROX, HEX,
Texas Red, TET, TRITC, TAMRA, cyanine-based dye and thiadicarbocyanine dye.
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According to embodiments of the present invention, the quencher moiety is selected from
the group consisting of Dabcyl, TAMRA, Eclipse, DDQ, QSY, Blackberry Quencher, Black Hole
Quencher, Qxl, Iowa black FQ, Iowa black RQ, and IRDye QC-1.
When more than one oligonucleotide is used, the sequence of the oligonucleotides may be
selected such that they hybridize to the same exon of the RNA determinant or different exons of
the RNA determinant. In one embodiment, at least one of the oligonucleotides hybridizes to the
3’ exon of the RNA determinant. In another embodiment, at least one of the oligonucleotides
hybridizes to the 5’ exon of the RNA determinant.
Classification of subjects into subgroups according to any of the aspects of the present
invention is preferably done with an acceptable level of clinical or diagnostic accuracy. An
"acceptable degree of diagnostic accuracy", is herein defined as a test or assay (such as the test
used in some aspects of the invention) in which the AUC (area under the ROC curve for the test
or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least
0.75, more preferably at least 0.80, and most preferably at least 0.85.
By a "very high degree of diagnostic accuracy", it is meant a test or assay in which the
AUC (area under the ROC curve for the test or assay) is at least 0.75, 0.80, desirably at least 0.85,
more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most
preferably at least 0.95.
Alternatively, the methods diagnose viral infections with at least 75% total accuracy, more
preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.
Alternatively, the methods diagnose viral infections with an MCC larger than 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 or 1.0.
For any of the aspects described herein above, preferably no more than 50 protein markers
are analyzed in a single test/analysis. In another embodiment, no more than 40 protein markers
are analyzed in a single test/analysis. In another embodiment, no more than 30 protein markers
are analyzed in a single test/analysis. In another embodiment, no more than 20 protein markers
are analyzed in a single test/analysis. In another embodiment, no more than 10 protein markers
are analyzed in a single test/analysis. In another embodiment, no more than 9 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 8 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 7 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 6 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 5 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 4 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 3 protein markers are
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analyzed in a single test/analysis. In another embodiment, no more than 2 protein markers are
analyzed in a single test/analysis. In another embodiment, no more than 1 protein marker is
analyzed in a single test/analysis.
For any of the aspects described herein above, preferably no more than 50 RNA markers
are analyzed in a single test/analysis. In another embodiment, no more than 40 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 30 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 20 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 10 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 9 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 8 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 7 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 6 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 5 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 4 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 3 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 2 RNA markers are
analyzed in a single test/analysis. In another embodiment, no more than 1 RNA marker is analyzed
in a single test/analysis.
Some aspects of the present invention also comprise a kit with a detection reagent that binds
to one or more determinant (e.g. protein or RNA). Also provided by the invention is an array of
detection reagents, e.g., antibodies that can bind to one or more protein determinants or
oligonucleotides that can bind to one or more RNA determinants). In one embodiment, the kit
contains antibodies and/or oligonucleotides that specifically bind to at least two, three, four, five,
six, seven, eight, nine or ten determinants which appear in any of Tables 1 and 7-12.
According to an exemplary embodiment, the kit (or array) does not specifically detect more
than 2 determinants, does not specifically detect more than 3 determinants, does not specifically
detect more than 4 determinants, does not specifically detect more than 5 determinants, does not
specifically detect more than 6 determinants does not specifically detect more than 7 determinants,
does not specifically detect more than 8 determinants, does not specifically detect more than 9
determinants, does not specifically detect more than 10 determinants.
Preferably, the kit does not contain components which specifically bind to more than 15, 20
, 30, 35, 40, 45, 50, 100 different or 200 different determinants.
Preferably, the concentration of the determinants is measured within about 24 hours after
sample is obtained. Alternatively, the concentration of the polypeptide-determinant is measured in
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0
a sample that was stored at 12 C or lower, when storage begins less than 24 hours after the sample
is obtained.
0
In some embodiments the sample could have been stored in either room temperature, 4 C,
0 0
-20 C or -80 C before measurement is performed.
In some embodiments the sample could have been stored for 1, 2, 3, 4, 5, 10, 12, 15, 20 or
24 hours before measurement is performed.
In some embodiments the sample may be stored for less than 5 minutes, 10 minutes, 20
minutes, 30 minutes, 45 minutes or 60 minutes before measurement is performed.
In some embodiments, the sample is collected in a serum separator tube (SST). Following
collection, the sample may be left at room temperature for at least 5, 10, 12, 15, 20, 25 30 minutes
to allow blood clotting and then centrifuged for about 5-30 minutes (e.g. at least 5, 10, 12, 15, 20,
, or 30 minutes) at 1200 x g or at about 3000 RPM.
Furthermore, traditional risk factors and additional clinical parameters may be used to
diagnose the viral disease of the non-symptomatic subject, to classify contagiousness or to rule in
a coronaviral infection or to determine the severity of the virus.
“Traditional laboratory risk factors” encompass biomarkers isolated or derived from
subject samples and which are currently evaluated in the clinical laboratory and used in traditional
global risk assessment algorithms, such as absolute neutrophil count (abbreviated ANC), absolute
lymphocyte count (abbreviated ALC), white blood count (abbreviated WBC), neutrophil %
(defined as the fraction of white blood cells that are neutrophils and abbreviated Neu (%)),
lymphocyte % (defined as the fraction of white blood cells that are lymphocytes and abbreviated
Lym (%)), monocyte % (defined as the fraction of white blood cells that are monocytes and
abbreviated Mon (%)),Sodium (abbreviated Na), Potassium (abbreviated K), Bilirubin
(abbreviated Bili).
“Clinical parameters” encompass all non-sample or non-analyte biomarkers of subject
health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE),
gender (Sex), core body temperature (abbreviated "temperature"), maximal core body temperature
since initial appearance of symptoms (abbreviated "maximal temperature"), time from initial
appearance of symptoms (abbreviated "time from symptoms") or family history (abbreviated
FamHX).
Combining the levels of particular markers is typically effected using algorithms or
formulas as described herein below.
A "formula," "algorithm," or "model" is any mathematical equation, algorithmic, analytical
or programmed process, or statistical technique that takes one or more continuous or categorical
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inputs (herein called "parameters") and calculates an output value, sometimes referred to as an
"index" or "index value". Non-limiting examples of "formulas" include sums, ratios, and
regression operators, such as coefficients or exponents, biomarker value transformations and
normalizations (including, without limitation, those normalization schemes based on clinical-
determinants, such as gender, age, or ethnicity), rules and guidelines, statistical classification
models, and neural networks trained on historical populations.
Of particular use in combining determinants are linear and non-linear equations and
statistical classification analyses to determine the relationship between levels of determinants
detected in a subject sample and the subject's risk assessment. In panel and combination
construction, of particular interest are structural and syntactic statistical classification algorithms,
and methods of index construction, utilizing pattern recognition features, including established
techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation,
Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear
Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF),
Recursive Partitioning Tree (RPART), as well as other related decision tree classification
techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees,
Neural Networks, Bayesian Networks, and Hidden Markov Models, among others. Other
techniques may be used in survival and time to event hazard analysis, including Cox, Weibull,
Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these
techniques are useful either combined with a determinant selection technique, such as forward
selection, backwards selection, or stepwise selection, complete enumeration of all potential panels
of a given size, genetic algorithms, or they may themselves include biomarker selection
methodologies in their own technique. These may be coupled with information criteria, such as
Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify
the tradeoff between additional biomarkers and model improvement, and to aid in minimizing
overfit. The resulting predictive models may be validated in other studies, or cross-validated in the
study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO)
and 10-Fold cross-validation (10-Fold CV).
Any formula may be used to combine TRAIL levels with the additional determinant into
indices useful in the practice of the invention. As indicated above, and without limitation, such
indices may indicate, among the various other indications, the probability, likelihood, absolute or
relative risk, time to or rate of conversion from one to another disease states, or make predictions
of future biomarker measurements of infection. This may be for a specific time period or horizon,
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or for remaining lifetime risk, or simply be provided as an index relative to another reference
subject population.
Although various preferred formulas are described here, several other model and formula
types beyond those mentioned herein and in the definitions above are well known to one skilled in
the art. The actual model type or formula used may itself be selected from the field of potential
models based on the performance and diagnostic accuracy characteristics of its results in a training
population.
Preferred formulas include the broad class of statistical classification algorithms, and in
particular the use of discriminant analysis. The goal of discriminant analysis is to predict class
membership from a previously identified set of features. In the case of linear discriminant analysis
(LDA), the linear combination of features is identified that maximizes the separation among
groups by some criteria. Features can be identified for LDA using an eigengene based approach
with different thresholds (ELDA) or a stepping algorithm based on a multivariate analysis of
variance (MANOVA). Forward, backward, and stepwise algorithms can be performed that
minimize the probability of no separation based on the Hotelling-Lawley statistic.
Eigengene-based Linear Discriminant Analysis (ELDA) is a feature selection technique
developed by Shen et al. (2006). The formula selects features (e.g. biomarkers) in a multivariate
framework using a modified eigen analysis to identify features associated with the most important
eigenvectors. "Important" is defined as those eigenvectors that explain the most variance in the
differences among samples that are trying to be classified relative to some threshold.
A support vector machine (SVM) is a classification formula that attempts to find a
hyperplane that separates two classes. This hyperplane contains support vectors, data points that
are exactly the margin distance away from the hyperplane. In the likely event that no separating
hyperplane exists in the current dimensions of the data, the dimensionality is expanded greatly by
projecting the data into larger dimensions by taking non-linear functions of the original variables
(Venables and Ripley, 2002). Although not required, filtering of features for SVM often improves
prediction. Features (e.g., biomarkers) can be identified for a support vector machine using a non-
parametric Kruskal-Wallis (KW) test to select the best univariate features. A random forest (RF,
Breiman, 2001) or recursive partitioning (RPART, Breiman et al., 1984) can also be used
separately or in combination to identify biomarker combinations that are most important. Both
KW and RF require that a number of features be selected from the total. RPART creates a single
classification tree using a subset of available biomarkers.
Other formula may be used in order to pre-process the results of individual determinant
measurement into more valuable forms of information, prior to their presentation to the predictive
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formula. Most notably, normalization of biomarker results, using either common mathematical
transformations such as logarithmic or logistic functions, as normal or other distribution positions,
in reference to a population's mean values, etc. are all well known to those skilled in the art. Of
particular interest are a set of normalizations based on clinical-determinants such as age, time from
symptoms, gender, race, or sex, where specific formula are used solely on subjects within a class
or continuously combining a clinical-determinants as an input. In other cases, analyte-based
biomarkers can be combined into calculated variables which are subsequently presented to a
formula.
In addition to the individual parameter values of one subject potentially being normalized,
an overall predictive formula for all subjects, or any known class of subjects, may itself be
recalibrated or otherwise adjusted based on adjustment for a population's expected prevalence and
mean biomarker parameter values, according to the technique outlined in D'Agostino et al, (2001)
JAMA 286:180-187, or other similar normalization and recalibration techniques. Such
epidemiological adjustment statistics may be captured, confirmed, improved and updated
continuously through a registry of past data presented to the model, which may be machine
readable or otherwise, or occasionally through the retrospective query of stored samples or
reference to historical studies of such parameters and statistics. Additional examples that may be
the subject of formula recalibration or other adjustments include statistics used in studies by Pepe,
M. S. et al, 2004 on the limitations of odds ratios; Cook, N. R., 2007 relating to ROC curves.
Finally, the numeric result of a classifier formula itself may be transformed post-processing by its
reference to an actual clinical population and study results and observed endpoints, in order to
calibrate to absolute risk and provide confidence intervals for varying numeric results of the
classifier or risk formula.
Some determinants may exhibit trends that depends on the patient age (e.g. the population
baseline may rise or fall as a function of age). One can use an `Age dependent normalization or
stratification` scheme to adjust for age related differences. Performing age dependent
normalization or stratification can be used to improve the accuracy of determinants for
differentiating between different types of infections. For example, one skilled in the art can
generate a function that fits the population mean levels of each determinant as function of age and
use it to normalize the determinant of individual subjects levels across different ages. Another
example is to stratify subjects according to their age and determine age specific thresholds or index
values for each age group independently.
As well as integrating age in order to optimize the above, the present inventors further
contemplate integrating patient comorbidities (background states) in order to optimize the above,
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Integrating patient specific clinical syndrome in order to optimize the above and/or integrating
additional clinical data to optimize the above.
As used herein the term “about” refers to 10 %.
The terms "comprises", "comprising", "includes", "including", “having” and their
conjugates mean "including but not limited to".
The term “consisting of” means “including and limited to”.
The term "consisting essentially of" means that the composition, method or structure may
include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or
parts do not materially alter the basic and novel characteristics of the claimed composition, method
or structure.
Throughout this application, various embodiments of this invention may be presented in a
range format. It should be understood that the description in range format is merely for
convenience and brevity and should not be construed as an inflexible limitation on the scope of
the invention. Accordingly, the description of a range should be considered to have specifically
disclosed all the possible subranges as well as individual numerical values within that range. For
example, description of a range such as from 1 to 6 should be considered to have specifically
disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3
to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This
applies regardless of the breadth of the range.
As used herein the term "method" refers to manners, means, techniques and procedures for
accomplishing a given task including, but not limited to, those manners, means, techniques and
procedures either known to, or readily developed from known manners, means, techniques and
procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical
arts.
As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing
or reversing the progression of a condition, substantially ameliorating clinical or aesthetical
symptoms of a condition or substantially preventing the appearance of clinical or aesthetical
symptoms of a condition.
In the context of the present invention, the following abbreviations may be used: ANC =
Absolute neutrophil count; ANN = Artificial neural networks; AUC = Area under the receiver
operating curve; BP = Bordetella pertussis; CHF = Congestive heart failure; CI = Confidence
interval; CID = Congenital immune deficiency; CLL = Chronic lymphocytic leukemia; CMV =
Cytomegalovirus; CNS = Central nervous system; COPD = Chronic obstructive pulmonary
disease; CP = Chlamydophila pneumonia; CRP = C-reactive protein; CSF = Cerebrospinal fluid;
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CV = Coefficient of variation; DOR = Diagnostic odds ratio; EBV = Epstein bar virus; eCRF =
Electronic case report form; ED = Emergency department, ELISA = Enzyme-linked
immunosorbent assay; FDR = False discovery rate; FMF = Familial Mediterranean fever; G-CSF
= Granulocyte colony-stimulating factor; GM-CSF = Granulocyte-macrophage colony-stimulating
factor; HBV = Hepatitis B virus; HCV = Hepatitis C virus; HI = Haemophilus influenza; HIV =
Human immunodeficiency virus; IDE = Infectious disease experts; IL = Interleukin; IRB =
institutional review board; IVIG = Intravenous immunoglobulin; KNN = K-nearest neighbors; LP
= Legionella pneumophila; LR+ = Positive likelihood ratio; LR- = Negative likelihood ratio; LRTI
= Lower respiratory tract infections; mAb = Monoclonal antibodies; MDD = Minimum detectable
dose; MDS = Myelodysplastic syndrome; MP = Mycoplasma pneumonia; MPD =
Myeloproliferative disease; NPV = Negative predictive value; PCT = Procalcitonin; PED =
Pediatric emergency department; PPV = Positive predictive value; QA = Quality assurance; RSV
= Respiratory syncytial virus; RV = Rhinovirus; SIRS = systemic inflammatory syndrome; SP =
Streptococcus pneumonia; STARD = Standards for Reporting of Diagnostic Accuracy; SVM =
Support vector machine; TNF = Tumor necrosis factor; URTI = Upper respiratory tract infection;
UTI = Urinary tract infection; WBC = White blood cell; WS = Wilcoxon rank-sum.
In the context of the present invention, the following statistical terms may be used:
“TP” is true positive, means positive test result that accurately reflects the tested-for
activity. For example in the context of the present invention a TP, is for example but not limited
to, truly classifying a bacterial infection as such.
“TN” is true negative, means negative test result that accurately reflects the tested-for
activity. For example in the context of the present invention a TN, is for example but not limited
to, truly classifying a viral infection as such.
“FN” is false negative, means a result that appears negative but fails to reveal a situation.
For example in the context of the present invention a FN, is for example but not limited to, falsely
classifying a bacterial infection as a viral infection.
“FP” is false positive, means test result that is erroneously classified in a positive category.
For example in the context of the present invention a FP, is for example but not limited to, falsely
classifying a viral infection as a bacterial infection.
“Sensitivity” is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.
“Specificity” is calculated by TN/(TN+FP) or the true negative fraction of non-disease or
normal subjects.
"Total accuracy" is calculated by (TN + TP)/(TN + FP +TP + FN).
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“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or the true positive
fraction of all positive test results. It is inherently impacted by the prevalence of the disease and
pre-test probability of the population intended to be tested.
“Negative predictive value” or “NPV” is calculated by TN/(TN + FN) or the true negative
fraction of all negative test results. It also is inherently impacted by the prevalence of the disease
and pre-test probability of the population intended to be tested. See, e.g., O’Marcaigh AS,
Jacobson RM, “Estimating The Predictive Value Of A Diagnostic Test, How To Prevent
Misleading Or Confusing Results,” Clin. Ped. 1993, 32(8): 485-491, which discusses specificity,
sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test.
"MCC" (Mathews Correlation coefficient) is calculated as follows: MCC = (TP * TN – FP
* FN) / {(TP + FN) * (TP + FP) * (TN + FP) * (TN + FN)}^0.5
where TP, FP, TN, FN are true-positives, false-positives, true-negatives, and false-negatives,
respectively. Note that MCC values range between -1 to +1, indicating completely wrong and
perfect classification, respectively. An MCC of 0 indicates random classification. MCC has been
shown to be a useful for combining sensitivity and specificity into a single metric (Baldi, Brunak
et al. 2000). It is also useful for measuring and optimizing classification accuracy in cases of
unbalanced class sizes (Baldi, Brunak et al. 2000).
“Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test
reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true
outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives
(FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive
values (PPV) or negative predictive values (NPV), Mathews correlation coefficient (MCC), or as
a likelihood, odds ratio, Receiver Operating Characteristic (ROC) curve, Area Under the Curve
(AUC) among other measures.
“Analytical accuracy” refers to the reproducibility and predictability of the measurement
process itself, and may be summarized in such measurements as coefficients of variation (CV),
Pearson correlation, and tests of concordance and calibration of the same samples or controls with
different times, users, equipment and/or reagents. These and other considerations in evaluating
new biomarkers are also summarized in Vasan, 2006.
“Performance” is a term that relates to the overall usefulness and quality of a diagnostic
or prognostic test, including, among others, clinical and analytical accuracy, other analytical and
process characteristics, such as use characteristics (e.g., stability, ease of use), health economic
value, and relative costs of components of the test. Any of these factors may be the source of
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superior performance and thus usefulness of the test, and may be measured by appropriate
“performance metrics,” such as AUC and MCC, time to result, shelf life, etc. as relevant.
By “statistically significant”, it is meant that the alteration is greater than what might be
expected to happen by chance alone (which could be a “false positive”). Statistical significance
can be determined by any method known in the art. Commonly used measures of significance
include the p-value, which presents the probability of obtaining a result at least as extreme as a
given data point, assuming the data point was the result of chance alone. A result is often
considered highly significant at a p-value of 0.05 or less.
The term “determinant” as used herein refers to a disease associated parameter or
biomarker.
According to yet another aspect of the present invention there is provided a method of
analyzing biological data, the biological data containing expression values of the TRAIL protein,
the IP10 protein, and the CRP protein in a body liquid sample of the subject, the method
comprising:
by a hardware processor, receiving the expression values, calculating a distance between a
segment of a curved line and an axis defined by a direction, determining a score classifying a
severity of a coronaviral infection of the subject based on said distance, and generating an output
indicative of said score;
wherein said distance is calculated at a point over said curved line defined by a coordinate
along said direction, said coordinate being defined based on the expression values,
wherein at least 90% of said segment is between a lower bound line f( )- 0 and an upper
bound line f()+1, wherein each of said 0 and said 1 is less than 0.5, and wherein said f( )
comprises a multiplication between a saturation function of said coordinate, and a saturation
function of the expression value of the CRP protein.
It is to be understood that, unless otherwise defined, the operations described hereinbelow
can be executed either contemporaneously or sequentially in many combinations or orders of
execution. Specifically, the ordering of the flowchart diagrams is not to be considered as limiting.
For example, two or more operations, appearing in the following description or in the flowchart
diagrams in a particular order, can be executed in a different order (e.g., a reverse order) or
substantially contemporaneously. Additionally, several operations described below are optional
and may not be executed.
FIG. 10 is a flowchart diagram of a method suitable for analyzing biological data obtained
from a subject, according to various exemplary embodiments of the present invention.
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Any of the methods described herein can be embodied in many forms. For example, it can
be embodied in on a tangible medium such as a computer for performing the method operations.
It can be embodied on a computer readable medium, comprising computer readable instructions
for carrying out the method operations. It can also be embodied in electronic device having digital
computer capabilities arranged to run the computer program on the tangible medium or execute the
instruction on a computer readable medium.
Computer programs implementing the method of the present embodiments can commonly
be distributed to users on a distribution medium such as, but not limited to, CD-ROMs or flash
memory media. From the distribution medium, the computer programs can be copied to a hard
disk or a similar intermediate storage medium. In some embodiments of the present invention,
computer programs implementing the method of the present embodiments can be distributed to
users by allowing the user to download the programs from a remote location, via a communication
network, e.g., the internet. The computer programs can be run by loading the computer instructions
either from their distribution medium or their intermediate storage medium into the execution
memory of the computer, configuring the computer to act in accordance with the method of this
invention. All these operations are well-known to those skilled in the art of computer systems.
The computational operations of the method of the present embodiments can be executed
by a computer, either remote from the subject or near the subject. When the computer is remote
from the subject, it can receive the data over a network, such as a telephone network or the Internet.
To this end, a local computer can be used to transmit the data to the remote computer. This
configuration allows performing the analysis while the subject is at a different location (e.g., at
home), and also allows performing simultaneous analyses for multiple subjects in multiple different
locations.
The computational operations of the method can also be executed by a cloud computing
resource of a cloud computing facility. The cloud computing resource can include a computing
server and optionally also a storage server, and can be operated by a cloud computing client as
known in the art.
The method according to some embodiments may be used to estimate the severity of a
coronaviral infection.
The biological data analyzed by the method contain expression values of a plurality of
proteins in a body sample of a subject. In some embodiments the biological data comprises
expression values of three or more proteins. In some embodiments the biological data comprises
expression values of at least CRP, TRAIL and IP-10.
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According to a particular embodiment, the levels of secreted (i.e. soluble) proteins (e.g.,
TRAIL, CRP and IP-10) are analyzed by the method.
Referring to FIG. 10, the method begins at 310 and continues to 311 at which a distance d
between a segment SROI of a curved line S and a non-curved axis is calculated.
The axis is defined by a direction. The distance between the segment of line S and axis
is calculated at a point P over the axis . P is defined by a coordinate denoted .
It is recognized that in the context of lines, coordinates are numbers that determine positions
on axes that are defined by directions. Usually, the terms "coordinate", "axis", and "direction" are
interchanged in the literature, but they actually represent different (but related) mathematical
objects. For example, in a Cartesian coordinate system, the letter x, is used to denote (i) the
horizontal axis, (ii) the rightward direction on this axis, and (iii) a point on this axis.
A formulation for obtaining the value of the coordinate is provided below. The
formulation when considered generally (namely as a function and not as a value returned by the
function) defines the direction along which the axis extends. Below, the direction along which
extends is are denoted using the same Greek letters as the coordinate , except that the direction
is denoted by an underlined Greek letter to indicate that it is a vector quantity. In this notation, the
axis extends along direction .
FIG. 11 illustrates the axis along direction . Also shown is a point P at coordinate . On
axis , along direction , there is a region-of-interest ROI which is a linear segment of axis
spanning from a minimal coordinate MIN to a maximal coordinate MAX along direction . The
point P is within the region-of-interest .
ROI
The distance d, calculated at 311, is measured from S to the point P, perpendicularly to .
The segment SROI of S is above the region-of-interest ROI. In other words, ROI is the projection
of SROI on . SROI is preferably a curved segment of the curve S.
The coordinate is defined by a combination of expression values of the proteins. For
example, can be a combinations of the expression values, according to the following equation:
= C + C D + C D +... +
0 1 1 2 2
where C , C C ... are constant and predetermined coefficients, each of the variables D , D , ... is
0 1, 2, 1 2
an expression value of one of the proteins (e.g., D1 can be the expression value of the TRAIL
protein, D can be the expression value of the IP-10 protein, and D can be the expression value of
2 3
the CRP protein), and is a function that is nonlinear with respect to at least one of the expression
values. C0 is referred to as an offset coefficient.
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In some embodiments of the present invention, prior to the calculation of the coordinate ,
each of the expression values is compared to one or more respective thresholds. In these
embodiments, the coordinate is defined at least based on the result of this comparison. For
example, each of the expression values can be compared to an upper threshold and a lower
threshold, wherein when the respective expression value is within a range defined by the lower and
upper thresholds, the respective expression value is used for calculating the coordinate , when the
respective expression value is outside this range but less than the lower threshold, the lower
threshold is used for calculating the coordinate instead of the respective expression value, and
when the respective expression value is outside this range but more than the upper threshold, the
upper threshold is used for calculating the coordinate instead of the respective expression value.
The lower threshold for the TRAIL protein is denoted TRAIL , and the upper threshold
min
for the TRAIL protein is denoted TRAILmax. Typical values for TRAILmin are from about 0 to
about 20 pg/ml, e.g., about 15 pg/ml, and typical values for TRAIL are from about 250 to about
max
350 pg/ml, e.g., about 300 pg/ml.
The lower threshold for the IP-10 protein is denoted IP10 , and the upper threshold for
min
the IP-10 protein is denoted IP10max. Typical values for IP10min are from about 0 to about 150
pg/ml, e.g., about 100 pg/ml, and typical values for IP10 are from about 5000 to about 7000
max
pg/ml, e.g., about 6000 pg/ml.
The lower threshold for the CRP protein is denoted CRP , and the upper threshold for the
min
CRP protein is denoted CRPmax. Typical values for CRPmin are from about 0 to about 1.5 mg/L,
e.g., about 1 mg/L, and typical values for CRP are from about 200 to about 300 mg/L, e.g., about
max
250 mg/L.
The present embodiments also contemplate an operation in which each of the expression
values is compared a confidence range of values, wherein when the respective expression value is
outside the confidence range, the method issues an error or warning message that the respective
expression value is outside the confidence range. A typical confidence range for the TRAIL protein
is from about -5 to about 1,000 pg/ml, a typical confidence range for the IP-10 protein is from about
-50 to about 30,000 pg/ml, and a typical confidence range for the CRP protein is from about -10 to
about 1,000 mg/L. Other confidence ranges are also contemplated.
The function is optional and may be set to zero (or, equivalently, not be used in the
calculation of the coordinate ). When =0 the coordinate is a linear combination of the proteins.
The nonlinear function can optionally and preferably be expressed as a sum of powers of the
expression values, for example, according to the following equation:
i
= iqiXi
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where i is a summation index, q is a set of coefficients, X {D , D , ...}, and i are numerical
i i 1 2
exponents. Note that the number of terms in does not necessarily equals the number of the
proteins, and that two or more terms in the sum may correspond to the same protein, albeit with a
different numerical exponent.
A representative example of the offset coefficient C0, suitable for the present embodiments,
is from about 0.9 to about 0.9 , where is defined as log(pmax/(1-pmax)) - log(pmin/(1-pmin))),
where pmax is a predetermined parameter which is more than 0.9 and less than 1 (e.g., 0.99), and
p is a predetermined parameter from about 0.01 to about 0.5. For example, when p is about
min max
0.99 and pmin is about 0.01, C0 is from about -8 to about 10, when pmax is about 0.99 and pmin is
about 0.2, C is from about -5 to about 6.9, and when p is about 0.99 and p is about 0.5, C is
0 max min 0
from about -3.67 to about 5.5. In some embodiments of the present invention C0 is about 0.93.
Herein, "log" means a natural logarithm.
A representative example of the coefficient C1, suitable in embodiments in which C1 is a
coefficient of the expression value of the TRAIL protein, is from about -0.04/TRAIL ml/pg
min
to about -0.04 /TRAILmin ml/pg, where and TRAILmin are as indicated above. For example,
when pmax is about 0.99, pmin is about 0.01 and TRAILmin is about 15 pg/ml, C1 is from about -0.65
ml/pg to about 0.57 ml/pg, when p is about 0.99, p is about 0.2 and TRAIL is about 15
max min min
pg/ml, C1 is from about -0.44 ml/pg to about 0.36 ml/pg, and when pmax is about 0.99, pmin is about
0.5 and TRAIL is about 15 pg/ml, C is from about -0.35 ml/pg to about 0.27 ml/pg. In some
min 1
embodiments of the present invention C1 is about -0.041 ml/pg.
A representative example of the coefficient C , suitable in embodiments in which C is a
2 2
coefficient of the expression value of the IP-10 protein, is from about 0.0006/IP10 ml/pg to
min
about 0.0006/IP10 ml/pg, where and IP10 are as indicated above. For example, when
min min
p is about 0.99, p is about 0.01 and IP10 is about 100 pg/ml, C is from about -0.1 ml/pg
max min min 2
to about 0.1 ml/pg, when pmax is about 0.99, pmin is about 0.2 and IP10min is about 100 pg/ml, C2 is
from about -0.06 ml/pg to about 0.06 ml/pg, and when pmax is about 0.99, pmin is about 0.5 and
IP10min is about 100 pg/ml, C2 is from about -0.04 ml/pg to about 0.05 ml/pg. In some embodiments
of the present invention C2 is about 0.0006 ml/pg.
A representative example of the coefficient C , suitable in embodiments in which C is a
3 3
coefficient of the expression value of the CRP protein, is from about 0.003/CRPmin L/mg to
about 0.003 /CRP L/mg, where p , p and CRP are as indicated above. For example,
min max min min
when p is about 0.99, p is about 0.01 and CRP is about 1 mg/L, C is from about -9.19
max min min 3
L/mg to about 9.19 L/mg, when pmax is about 0.99, pmin is about 0.2 and CRPmin is about 1 mg/L,
C is from about -5.98 L/mg to about 5.98 L/mg, and when p is about 0.99, p is about 0.5 and
3 max min
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CRP is about 1 mg/L, C is from about -4.6 L/mg to about 4.6 L/mg. In some embodiments of
min 3
the present invention C3 is about 0.0025 L/mg.
The boundaries MIN, and MAX of ROI preferably correspond to the physiologically
possible ranges of the expression values of the proteins, according to a protocol used for measuring
those expression values. For example, in measurements performed by the Inventors using an
ELISA protocol, typical physiologically possible ranges are from 0 to about 400 ug/ml for CRP,
from 0 to about 3000pg/ml for IP-10, and from 0 to about 700 pg/ml for TRAIL. Some subjects
may exhibit concentrations that lie outside these ranges. It is appreciated that when the expression
values of TRAIL, CRP and IP-10 are measured by other techniques or protocols the values of the
boundaries MIN, and MAX of ROI, and optionally also the coefficients C0, C1, C2, C3, may be
different that the above exemplified values.
At least a major part of the segment S of curved line S is between two curved lines
ROI
referred to below as a lower bound curved line SLB and an upper bound curved line SUB.
As used herein “major part of the segment SROI” refers to a part of a smoothed version SROI
whose length is 60% or 70% or 80% or 90% or 95% or 99% of a smoothed version of the length
of SROI.
As used herein, “a smooth version of the segment S ” refers to the segment S ,
ROI ROI
excluding regions of SROI at the vicinity of points at which the Gaussian curvature is above a
curvature threshold, which is X times the median curvature of S , where X is 1.5 or 2 or 4 or 8.
ROI
The following procedure can be employed for the purpose of determining whether the major
part of the segment S is between S and S . Firstly, a smoothed version of the segment S
ROI LB UB ROI
is obtained. Secondly, the length A1 of the smoothed version of the segment SROI is calculated.
Thirdly, the length A of the part of the smoothed version of the segment S that is between S
2 ROI LB
and SUB is calculated. Fourthly, the percentage of A2 relative to A1 is calculated.
FIGs. 12A-D illustrates a procedure for obtaining the smooth version of S . The Gaussian
ROI
curvature is calculated for a sufficient number of sampled points on SROI. For example, when the
line is represented as a set of points, the Gaussian curvature can be calculated for the points in the
set. The median of the Gaussian curvature is then obtained, and the curvature threshold is
calculated by multiplying the obtained median by the factor X. FIG. 12A illustrates S before
ROI
the smoothing operation. Marked is a region 320 having one or more points 322 at which the
Gaussian curvature is above the curvature threshold. The point or points at which the Gaussian
curvature is maximal within region 320 is removed and region 320 is smoothly interpolated, e.g.,
via polynomial interpolation (FIG. 12B). The removal and interpolation is repeated iteratively
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(FIG. 12C) until the segment S does not contain regions at which the Gaussian curvature is
ROI
above the curvature threshold (FIG. 12D).
The lower and upper bound curved lines S and S can be written in the form:
LB UB
SLB = f( )- 0,
S = f( )+
UB 1
where f( ) is a classification function of the coordinate (along the direction ) which represents
the severity of a coronaviral infection of the subject. In some embodiments of the invention f( )
comprises a multiplication between a saturation function p of the coordinate , and a saturation
function w of the expression value of the CRP protein.
A saturation function, as used herein, is a monotonically increasing function which exhibits
a plateau for sufficiently large value of its argument. The plateau is preceded by a segment at
which the second derivative of the saturation function changes its sign. At the plateau, the first
derivative of the saturation function monotonically decreases for any value of the argument above
a predetermined value.
In some embodiments of the present invention the saturation function p of the coordinate
is linearly proportional to 1/(1+Exp(-)). In some embodiments of the present invention p =
1/(1+Exp(- )). In these embodiments, setting the coefficients C0, C1, etc., based on the
predetermined parameters pmin and pmax as further detailed hereinabove, ensures that that a change
of the coefficient value to the upper limit of the respective range or to the lower limit of the
respective range translates to a change in p from p to p .
min max
In some embodiments of the present invention the saturation function w of the expression
-h
value of the CRP protein is linearly proportional to 1/(1+(CRP/CRP ) ), where CRP and h are
0 0
predetermined shift and width parameters. Representative examples of a value for the shift
parameter CRP is from about 1 mg/L to about 1000 mg/L, or from about 100 mg/L to about 500
0
mg/L, or from about 200 mg/L to about 300 mg/L, e.g., about 260 mg/L. Representative examples
of a value for the width parameter h is from about 2 to about 100, or from about 2 to about 50, or
from about 2 to about 10, e.g., about 6. In some embodiments of the present invention
-h
w=1/(1+(CRP/CRP ) ).
0
The classification function optionally and preferably also includes at least one term that
does not depend on w and/or at least one term that does not depend on p. In experiments performed
by the Inventors it was found that when the classification function is in the form f( )= p(1-w)+w,
a more accurate classification of the severity of the coronaviral infection is obtained.
In any of the above embodiments each of the parameters 0 and 1 is less than 0.5 or less
than 0.4 or less than 0.3 or less than 0.2 or less than 0.1 or less than 0.05.
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Referring again to FIG. 10, the method proceeds to 312 at which a score classifying the
severity of a coronaviral infection of the subject is determined based on the distance d. For
example, the score can be the calculated distance or some proxy thereof, e.g., a value that is
proportional to d. In some embodiments, the score is defined as Round(100d), where Round is a
function that returns the nearest integer of its argument (e.g., Round(40.1)=40, and
Round(40.51)=41).
The method optionally and preferably continues to 313 at which an output indicative of the
score is generated. The output can be presented as text, and/or graphically and/or using a color
index.
The output can in some embodiments of the present invention be a description of the score,
rather than the score itself. As a representative example, which is not to be considered as limiting,
when the score ranges from 0 to 100, the method can output an indication that there is a very low
likelihood for severe outcome for scores between 0 and about 20, an indication that there is a low
likelihood for severe outcome for scores between about 20 and about 40, an indication that there is
a moderate likelihood for severe outcome for scores between about 40 and about 80, and an
indication that there is a high likelihood for severe outcome for scores between about 80 and about
100.
In some embodiments of the present invention, the subject is treated (314) for the
coronaviral infection based on the output generated at 313. For example, when the score is above
a predetermined severity threshold, the subject can be treated by , e.g., mechanical ventilation, life
support, catheterization, hemofiltration, invasive monitoring, sedation, intensive care admission,
surgical intervention, drug of last resort, and the like.
The method ends at 315.
FIG. 13 is a schematic illustration of a client computer 130 having a hardware processor
132, which typically comprises an input/output (I/O) circuit 134, a hardware central processing
unit (CPU) 136 (e.g., a hardware microprocessor), and a hardware memory 138 which typically
includes both volatile memory and non-volatile memory. CPU 136 is in communication with I/O
circuit 134 and memory 138. Client computer 130 preferably comprises a graphical user interface
(GUI) 142 in communication with processor 132. I/O circuit 134 preferably communicates
information in appropriately structured form to and from GUI 142. Also shown is a server
computer 150 which can similarly include a hardware processor 152, an I/O circuit 154, a hardware
CPU 156, a hardware memory 158. I/O circuits 134 and 154 of client 130 and server 150 computers
can operate as transceivers that communicate information with each other via a wired or wireless
communication. For example, client 130 and server 150 computers can communicate via a network
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140, such as a local area network (LAN), a wide area network (WAN) or the Internet. Server
computer 150 can be in some embodiments be a part of a cloud computing resource of a cloud
computing facility in communication with client computer 130 over the network 140. Further
shown, is a measuring system 146 that is associated with client computer 130, and that measures
the amount of the proteins in the body liquid sample of the subject blood, for example, e.g., using
one or more of the techniques described herein.
GUI 142 and processor 132 can be integrated together within the same housing or they can
be separate units communicating with each other. Similarly, system 146 and processor 132 can be
integrated together within the same housing or they can be separate units communicating with each
other.
GUI 142 can optionally and preferably be part of a system including a dedicated CPU and
I/O circuits (not shown) to allow GUI 142 to communicate with processor 132. Processor 132
issues to GUI 142 graphical and textual output generated by CPU 136. Processor 132 also receives
from GUI 142 signals pertaining to control commands generated by GUI 142 in response to user
input. GUI 142 can be of any type known in the art, such as, but not limited to, a keyboard and a
display, a touch screen, and the like. In some embodiments, GUI 142 is a GUI of a mobile device
such as a smartphone, a tablet, a smartwatch and the like. When GUI 142 is a GUI of a mobile
device, the CPU circuit of the mobile device can serve as processor 132 and can execute the code
instructions pertaining to the method described herein.
Client 130 and server 150 computers can further comprise one or more computer-readable
storage media 144, 164, respectively. Media 144 and 164 are preferably non-transitory storage
media storing computer code instructions for executing the method as further detailed herein, and
processors 132 and 152 execute these code instructions. The code instructions can be run by
loading the respective code instructions into the respective execution memories 138 and 158 of the
respective processors 132 and 152.
Each of storage media 144 and 164 can store program instructions which, when read by the
respective processor, cause the processor to receive the expression values of the proteins and to
execute the method as described herein. In some embodiments of the present invention, the
expression values of the proteins are generated as digital data by system 146 and are transmitted to
processor 132 by means of I/O circuit 134. Processor 132 receives the expression values, calculates
the distance between the segment of a curved line and the axis, as further detailed hereinabove,
determines the score as further detailed hereinabove and displays the score, for example, on GUI
142. Alternatively, processor 132 can transmit the expression values over network 140 to server
computer 150. Computer 150 receives the expression values, calculates the distance between the
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segment of a curved line and the axis, and determines the score as further detailed hereinabove,
and transmits the score back to computer 130 over network 140. Computer 130 receives the score
and displays it on GUI 142.
It is appreciated that certain features of the invention, which are, for clarity, described in
the context of separate embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which are, for brevity, described in the
context of a single embodiment, may also be provided separately or in any suitable subcombination
or as suitable in any other described embodiment of the invention. Certain features described in
the context of various embodiments are not to be considered essential features of those
embodiments, unless the embodiment is inoperative without those elements.
Various embodiments and aspects of the present invention as delineated hereinabove and
as claimed in the claims section below find experimental support in the following examples.
EXAMPLES
Reference is now made to the following examples, which together with the above
descriptions illustrate some embodiments of the invention in a non limiting fashion.
Generally, the nomenclature used herein and the laboratory procedures utilized in the
present invention include molecular, biochemical, microbiological and recombinant DNA
techniques. Such techniques are thoroughly explained in the literature. See, for example,
"Molecular Cloning: A laboratory Manual" Sambrook et al., (1989); "Current Protocols in
Molecular Biology" Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols
in Molecular Biology", John Wiley and Sons, Baltimore, Maryland (1989); Perbal, "A Practical
Guide to Molecular Cloning", John Wiley & Sons, New York (1988); Watson et al., "Recombinant
DNA", Scientific American Books, New York; Birren et al. (eds) "Genome Analysis: A
Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998);
methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and
,272,057; "Cell Biology: A Laboratory Handbook", Volumes I-III Cellis, J. E., ed. (1994);
"Culture of Animal Cells - A Manual of Basic Technique" by Freshney, Wiley-Liss, N. Y. (1994),
Third Edition; "Current Protocols in Immunology" Volumes I-III Coligan J. E., ed. (1994); Stites
et al. (eds), "Basic and Clinical Immunology" (8th Edition), Appleton & Lange, Norwalk, CT
(1994); Mishell and Shiigi (eds), "Selected Methods in Cellular Immunology", W. H. Freeman and
Co., New York (1980); available immunoassays are extensively described in the patent and
scientific literature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578;
3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074;
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4,098,876; 4,879,219; 5,011,771 and 5,281,521; "Oligonucleotide Synthesis" Gait, M. J., ed.
(1984); “Nucleic Acid Hybridization" Hames, B. D., and Higgins S. J., eds. (1985); "Transcription
and Translation" Hames, B. D., and Higgins S. J., eds. (1984); "Animal Cell Culture" Freshney,
R. I., ed. (1986); "Immobilized Cells and Enzymes" IRL Press, (1986); "A Practical Guide to
Molecular Cloning" Perbal, B., (1984) and "Methods in Enzymology" Vol. 1-317, Academic
Press; "PCR Protocols: A Guide To Methods And Applications", Academic Press, San Diego, CA
(1990); Marshak et al., "Strategies for Protein Purification and Characterization - A Laboratory
Course Manual" CSHL Press (1996); all of which are incorporated by reference as if fully set forth
herein. Other general references are provided throughout this document. The procedures therein
are believed to be well known in the art and are provided for the convenience of the reader. All the
information contained therein is incorporated herein by reference.
EXAMPLE 1
Diagnosing non-symptomatic subjects.
In the presented analysis, serial measurements of TRAIL, CRP and IP-10 were performed
on a non-symptomatic individual who had been in close contact with a virally infected patient. On
day 3 the patient developed upper respiratory tract symptoms. TRAIL, CRP and IP-10 levels were
shown to increase from basal level prior to symptoms onset. More specifically, a 25% and 50%
increase in TRAIL and IP-10 levels, respectively, were found to accurately detect the non-
symptomatic individual prior to symptoms onset (Figures 1A-C).
EXAMPLE 2
The ability of TRAIL, CRP and IP10 to distinguish between non-infectious subjects from
virally infected subjects within 1 day from symptom onset was analyzed. Protein levels were
significantly differentially expressed across different viruses (adenovirus; CRP p-value=0.002, IP-
p-value<0.001, TRAIL p-value<0.001, bocavirus; CRP p-value=0.01, IP-10 p-value=0.007,
TRAIL p-value<0.001, coronavirus; CRP p-value=0.01, IP-10 p-value<0.001, TRAIL p-
value<0.001, enterovirus; CRP p-value<0.001, IP-10 p-value<0.001, TRAIL p-value<0.001,
respiratory syncytial virus; CRP p-value=0.007, IP-10 p-value<0.001, TRAIL p-value<0.001,
metapneumovirus; CRP p-value=0.02, IP-10 p-value<0.001, TRAIL p-value<0.001, parainfluenza
virus; CRP p-value=0.007, IP-10 p-value<0.001, TRAIL p-value<0.001).
Data derived from the comparison of viral patients tested at time from symptoms <=1 (day
0 or day 1) to non-infectious patients allowed for the estimation of cutoffs for predicting whether
a non-symptomatic subject has a virus. The cutoffs are provided in Table 13, herein below.
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Table 13
Biomarker Cutoff
TRAIL (pg/ml) 100
CRP (mg/L) 5
IP-10 (pg/ml) 200
EXAMPLE 3
Analyzing contagiousness of non-symptomatic subjects.
In the presented cohort, 81 non-symptomatic individuals were analyzed. Using multiplex
PCR applied to nasal swabs, 21 were found to be positive for rhinovirus. TRAIL and IP-10 levels
were correlated with viral loads (Figures 2A-B).
Furthermore, TRAIL, IP-10 and CRP were shown to accurately differentiate low vs high
viral loads and thus could be used to inform a non-symptomatic individual contagiousness status
(Table 14).
Table 14. TRAIL, IP-10 and CRP single/triplet’s accuracy for differentiating non-symptomatic
individuals with high and low viral loads.
Feature #1 Feature #2 Feature #3 AUC
CRP 0.598
IP-10 0.893
TRAIL 0.786
CRP IP-10 TRAIL 0.911
Pairs of determinants were also tested. CRP + IP10, CRP + TRAIL and IP10 + TRAIL all
gave AUCs above 0.85, with the highest being for CRP +IP10.
EXAMPLE 4
Correlation of IP10 levels with patient status
The level of IP10 was measured for a SARS-COV-2 positive male patient having the
following comorbidities: Hypothyroidism, Hypercholesterol, Osteoporosis. The patient was
th
diagnosed with Covid 19 on 31/3/2020. The patient was treated with steroid on 9 of April, and
th.
the dose was lowered on 11 April.
The results are illustrated in Figure 3.
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EXAMPLE 5
Study Design
The prospective study included a cohort of SARS-CoV-2 positive patients recruited at a
COVID-19 dedicated medical center (n = 52). An additional retrospective study was conducted to
enable comparison of IP-10 levels across other acute respiratory infections (n = 182) and healthy
subjects (n = 98).
Patient population
COVID-19 patients
The prospective study included 52 SARS-CoV-2 positive patients recruited at a COVID-
19 dedicated medical centre. ARDS and its severity were defined according to the Berlin
definition.24
Patients with other respiratory viruses
A retrospective analysis was performed on a cohort of adult patients (n = 182) pooled from
14
2 studies, Curiosity (NCT01917461) and Observer (NCT03011515) who fulfilled all of the
following inclusion criteria: meet inclusion/exclusion criteria of original study; detection of at least
one of the following viruses (human rhinovirus [HRV], respiratory syncytial virus [RSV],
Influenza A [Flu A], Influenza B [Flu B], human coronavirus [HCoV]); and adjudication as viral
infection by an expert panel (no bacterial detection).
Healthy subjects
A retrospective analysis was performed on a cohort of adult patients (n = 98) from
14
Curiosity (NCT01917461) and Observer (NCT03011515) who were afebrile with no apparent
infectious disease.
Clinical decision support protocol
An overview of the clinical decision support protocol is provided in Figure 4. In brief, to
manage oxygenation, PaO2/FiO2 (P/F, ratio of arterial oxygen partial pressure to fractional
inspired oxygen) was monitored twice daily and positive end-expiratory pressure (PEEP) titration
was made based on cardiac output measurements when indicated. All patients received high dose
N-acetylcysteine and bromohexin inhalations in order to induce mucolysis and prevent sticky
secretion and atelectasis. Intra-tracheal injected surfactant was an option as a compassionate
therapy for refractory hypoxemic ventilated patients.
Echocardiography and bedside Doppler ultrasound were ordered when pulmonary emboli
or deep vein thrombosis were clinically suspected and when D-dimer levels significantly
increased. Troponin levels were measured daily and electrocardiogram measurements were
performed routinely to detect QT prolongation. All ICU patients received 40 mg twice daily of
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enoxaparin. D-dimers were measured daily and the enoxaparin dose was adjusted up to 60 mg
twice daily when D-dimer levels increased significantly. In case of proven thrombosis (as
visualized by imaging), full anti-coagulation therapy was recommended. When minor bleeding
occurred, sub-cutaneous heparin was administered.
All ICU patients considered as hyperinflammatory were administered a total tocilizumab
dosing of 800 mg, given in 2 separate doses of 400 mg, 12 hours apart. The following parameters
were considered as reflective of a hyperinflammatory state: C-reactive protein (CRP) > 90 mg/l,
ferritin >500 ng/dl, and IP-10 >1,000 pg/ml. All hyperinflammatory ICU patients were
administered methylprednisolone or equivalent dosing of another corticosteroid; basic dosing was
1.5 mg/kg divided into 3 doses. Patients on hemodialysis who were hyper-inflamed had access to
cytokine removal hemodialysis (Theranova dialyzer).
The anti-viral therapy was based on combinations of hydroxychloroquine, azithromycin,
zinc, and lopinavir/ritonavir at the discretion of the infectious disease consultant and was discussed
daily. Patients who did not show clinical improvement were given convalescent plasma divided
into two doses as a compassionate therapy. Compassionate Remdesivir was given to only one
patient for a total of 7 days (200 mg loading dose followed by 100 mg maintenance dose from day
2 onwards) and was stopped as a result of elevated liver function tests.
Laboratory procedures
Blood was collected serially from 52 patients as part of routine blood draw into serum
separating tubes (SST). Serum was separated within 2 hours of collection and the fresh serum
samples were measured on the MeMed Key™ (MeMed, Israel) rapid immunoassay platform that
provides measurements of 3 immune system proteins (IP-10, CRP, and TNF-related apoptosis
inducing ligand [TRAIL]), in 15 minutes, based on MeMed BV™ (MeMed, Israel) testing
cartridges. Any unused serum was frozen at -20°C for up to one week and then transferred for
storage at -80°C.
Frozen serum was used to determine retrospectively blood concentrations of IL-6.
Measurements were performed using ‘Quantikine human IL-6’ (R&D systems, MN, USA) ELISA
assay kits. Due to high IL-6 concentrations, each serum sample was diluted 1:4 and 1:40 with
RD6F calibrator diluent supplied in the kits. Results were averaged when the difference in
concentration between the 2 dilutions was <20%, otherwise the dilution in the linear range of the
calibration curve was taken. Measurements were performed using an Absorbance Microplate
Reader Sunrise and Magellan program (Tecan life science Grödig, Austria).
SARS-CoV-2 positivity was determined by RT-PCR (Seegene Inc, South Korea).
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Statistical analysis
Continuous variables are reported as median (interquartile range). Values for 2 related
variables were compared using Mann-Whitney-U test (for continuous values) and Fisher’s Exact
test (for discrete values). Steroid dosage was converted to methylprednisolone equivalent dosage,
using MedCalc's Steroid Conversion Calculator.25. The analysis was performed using Python
3.7.6.
RESULTS
Patient population
Overall, 502 serial blood draws were sampled from 52 SARS-CoV-2 patients that were
admitted to the COVID-19 dedicated medical centre between 7th April 2020 and 10th May 2020
and included in the cohort study, with 12 admitted to the ICU. The median age of the 52 patients
was 69, 69% were male, 23% were ventilated, 4 died. There was low in-ICU mortality (1 death,
8%) and non-pulmonary organ failure (0%); the 90-day mortality for ICU patients was 25% (3
deaths). The most common comorbidities were diabetes (42%) and hypertension (46%) (Table
15). Among the 12 ICU patients, 10 needed invasive mechanical ventilation and exhibited a
pulmonary status compatible with severe ARDS (Table 16). Of these, 7 were weaned during their
ICU stay and transferred to a rehabilitation center, 1 was transferred to a respiratory rehabilitation
center (Patient #5) and 2 (Patients #3 and #9) were transferred to the internal medicine department,
where patient #3 died. 8 patients developed secondary bacterial infection (Table 16).
Table 15
All Non-ICU ICU p-
(n=52) (n=40) (n=12) value
General
Age
68.6 (16.7) 66.4 (20.0) 70.2 (13.2) 0.313
Female 16 (31.0%) 15 (38.0%) 1 (8.0%) 0.078
Smoking Status
Smoker 3 (6.0%) 3 (8.0%) 0 (0.0%) 1
Past Smoker 3 (6.0%) 3 (8.0%) 0 (0.0%) 1
Other
Death 4 (8.0%) 1 (2.0%) 3 (25.0%) 0.034
Discharged 51 (98.0%) 39 (98.0%) 12 (100.0%) 1
Highest Temp 37.4 (1.2) 37.3 (0.9) 38.3 (0.9) <0.001
Duration of Hospital Stay 11.0 (13.5) 9.0 (7.5) 26.5 (12.2) <0.001
Time between PCR Positive and BV Test (days) 4.5 (6.0) 3.0 (5.0) 8.0 (8.8) 0.003
ICU 12 (23.0%) 0 (0.0%) 12 (100.0%) <0.001
Ventilated 12 (23.0%) 2 (5.0%) 10 (83.0%) <0.001
Time between Symptoms and BV Test (days) 7.0 (10.2) 6.0 (10.0) 14.0 (7.0) 0.063
Time between Symptoms and ICU Admission 8.0 (4.0) 8.0 (4.0)
Time between Symptoms and PCR Positive 2.5 (5.0) 2.0 (4.5) 4.0 (5.2) 0.343
Time on Ventilation (days) 0.0 (0.0) 0.0 (0.0) 15.0 (11.0) <0.001
Time between PCR Positive and ICU Admission 4.5 (2.2) 4.5 (2.2)
Time between PCR Positive and PCR Negative 19.0 (17.2) 8.5 (10.0) 26.5 (10.0) 0.015
Co-infection
Bacterial Infection 10 (19.0%) 2 (5.0%) 8 (67.0%) <0.001
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All Non-ICU ICU p-
(n=52) (n=40) (n=12) value
Co-infection 10 (19.0%) 2 (5.0%) 8 (67.0%) <0.001
Fungal Infection 4 (8.0%) 0 (0.0%) 4 (33.0%) 0.002
Conditions
Diabetes 22 (42.0%) 16 (40.0%) 6 (50.0%) 0.74
Cardiovascular Disease 9 (17.0%) 7 (18.0%) 2 (17.0%) 1
Cerebrovascular disease 3 (6.0%) 3 (8.0%) 0 (0.0%) 1
Chronic Lung Disease 7 (13.0%) 6 (15.0%) 1 (8.0%) 1
Chronic renal disease 6 (12.0%) 4 (10.0%) 2 (17.0%) 0.612
Dyslipidemia 9 (17.0%) 6 (15.0%) 3 (25.0%) 0.415
Hypertension 24 (46.0%) 18 (45.0%) 6 (50.0%) 1
Hypothyroidism 6 (12.0%) 2 (5.0%) 4 (33.0%) 0.021
Malignancy 5 (10.0%) 2 (5.0%) 3 (25.0%) 0.074
Obesity (BMI>30 kg/m2) 9 (17.0%) 8 (20.0%) 1 (8.0%) 0.666
Immunodeficiency 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Symptoms
Sore throat 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Vomiting 2 (4.0%) 1 (2.0%) 1 (8.0%) 0.412
Urinary Complaints 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Sputum production 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Weakness 12 (23.0%) 11 (28.0%) 1 (8.0%) 0.253
Rhinorrhea 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Lethargy 2 (4.0%) 2 (5.0%) 0 (0.0%) 1
Agitation 1 (2.0%) 1 (2.0%) 0 (0.0%) 1
Chest pain 2 (4.0%) 2 (5.0%) 0 (0.0%) 1
Confusion 2 (4.0%) 2 (5.0%) 0 (0.0%) 1
Chills 1 (2.0%) 0 (0.0%) 1 (8.0%) 0.231
Diarrhea 6 (12.0%) 6 (15.0%) 0 (0.0%) 0.316
Dyspnea 23 (44.0%) 17 (42.0%) 6 (50.0%) 0.746
Fatigue 3 (6.0%) 2 (5.0%) 1 (8.0%) 0.553
Fever 29 (56.0%) 18 (45.0%) 11 (92.0%) 0.007
Headache 5 (10.0%) 4 (10.0%) 1 (8.0%) 1
Loss of Taste and Smell 5 (10.0%) 5 (12.0%) 0 (0.0%) 0.578
Myalgia 4 (8.0%) 4 (10.0%) 0 (0.0%) 0.562
Nausea 3 (6.0%) 3 (8.0%) 0 (0.0%) 1
Cough 26 (50.0%) 17 (42.0%) 9 (75.0%) 0.097
Markers
AST (GOT) (u/l) max 37.0 (38.8) 28.4 (25.5) 86.0 (91.2) 0
ALT (GPT) (u/l) max 34.2 (70.1) 28.6 (27.0) 120.1 (147.1) 0
Albumin (g/dl) max 3.8 (0.5) 3.8 (0.5) 3.6 (0.3) 0.113
INR max 1.1 (0.2) 1.1 (0.2) 1.2 (0.2) 0.037
Glucose (mg/dl) max 153.5 (129.6) 126.4 (85.1) 247.0 (76.9) <0.001
LDH (u/l) max 610.0 (347.5) 500.0 (244.5) 912.5 (263.8) <0.001
Lymph.abs (K/micl) max 1.3 (0.8) 1.2 (0.6) 2.0 (3.9) 0.03
Lymph.abs (K/micl) min 0.8 (0.7) 0.8 (0.7) 0.4 (0.4) 0.003
Neu.abs (K/micl) max 5.6 (5.6) 5.0 (3.0) 13.8 (6.2) <0.001
Neu.abs (K/micl) min 3.1 (1.9) 3.1 (2.4) 3.0 (1.1) 0.214
PLT (10^3) max 375.0 (193.8) 319.0 (219.2) 408.5 (71.5) 0.062
PLT (10^3) min 186.5 (111.8) 228.0 (123.5) 139.5 (49.8) 0.001
PaO2/FiO2 max 284.0 (163.5) 166.0 (0.0) 313.5 (153.0) <0.001
Total protein (g/dl) max 7.0 (0.7) 7.0 (0.6) 7.0 (0.8) 0.136
PaO2/FiO2 min 83.0 (56.5) 141.0 (0.0) 80.5 (42.0) <0.001
Troponin max 19.5 (44.5) 11.5 (18.5) 69.5 (55.5) <0.001
Urea max 46.3 (40.6) 37.2 (27.5) 105.0 (66.9) <0.001
Ferritin min 283.0 (365.4) 277.2 (421.7) 330.2 (216.8) 0.368
Ferritin max 595.0 462.0 (459.2) 1691.5 (916.8) <0.001
D-Dimer max 1319.0 1140.0 7235.5 <0.001
Creatinine (mg/dl) max 1.0 (0.4) 0.9 (0.4) 1.0 (0.5) 0.086
Bili total (mg/dl) max 0.5 (0.5) 0.5 (0.4) 0.9 (0.5) 0.001
CRP min (mg/l) 19.6 (56.2) 29.9 (67.4) 2.2 (3.8) <0.001
Median CRP (mg/l) 70.9 (85.4) 77.8 (70.1) 36.0 (166.8) 0.5
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All Non-ICU ICU p-
(n=52) (n=40) (n=12) value
CRP max (mg/l) 97.9 (122.8) 95.0 (92.1) 158.4 (172.6) 0.04
Scores
COVID-19 Severity 26 (50.0%) 14 (35.0%) 12 (100.0%) <0.001
qSOFA (on admission) 0.0 (1.0) 0.0 (1.0) 1.0 (1.2) 0.003
Treatments
Solumedrol 12 (23.0%) 1 (2.0%) 11 (92.0%) <0.001
Hydrocortisone 10 (19.0%) 4 (10.0%) 6 (50.0%) 0.006
Systemic Steroids (IV/PO) 22 (42.0%) 10 (25.0%) 12 (100.0%) <0.001
Renal Replacement Therapy 2 (4.0%) 1 (2.0%) 1 (8.0%) 0.412
Remdesivir 1 (2.0%) 0 (0.0%) 1 (8.0%) 0.231
Prednisone 6 (12.0%) 6 (15.0%) 0 (0.0%) 0.316
Nitric Oxide 5 (10.0%) 0 (0.0%) 5 (42.0%) <0.001
ECMO 1 (2.0%) 0 (0.0%) 1 (8.0%) 0.231
Vasopressors 11 (21.0%) 2 (5.0%) 9 (75.0%) <0.001
Convalescent Plasma 7 (13.0%) 3 (8.0%) 4 (33.0%) 0.041
Azithromycin 35 (67.0%) 24 (60.0%) 11 (92.0%) 0.076
Antiviral Lopinavir/Ritonavir 7 (13.0%) 1 (2.0%) 6 (50.0%) <0.001
Antiviral Hydroxychloroquine plus Azithromycin 33 (63.0%) 22 (55.0%) 11 (92.0%) 0.037
Antiviral Hydroxychloroquine 36 (69.0%) 24 (60.0%) 12 (100.0%) 0.01
Antibiotics 30 (58.0%) 19 (48.0%) 11 (92.0%) 0.008
Tocilizumab 13 (25.0%) 3 (8.0%) 10 (83.0%) <0.001
Days under Systemic Steroids 0.0 (6.2) 0.0 (0.2) 7.5 (7.5) <0.001
BMI, Body mass index; AST, Aspartate transaminase; AL, Alanine transaminase; LDH, Lactate
dehydrogenase; Lymph. Abs, Absolute lymphocytes; Neu. Abs, Absolute neutrophils; PLT,
platelets; PaO2/FiO2, Ratio of arterial oxygen partial pressure to fractional inspired oxygen;
ECMO, Extracorporeal membrane oxygenation; qSOFA, quick sequential organ failure
assessment; TRAIL, TNF-related apoptosis inducing ligand; IP-10, interferon-γ induced protein
(also known as CXCL-10); CRP, C-reactive protein.
Table 16
1 2 3 4 5 6 7 8 9 11 18 38
ICU patient #
48 69 76 63 61 46 71 76 68 76 73 84
Age
Ma Ma Ma Ma Ma Ma Ma Ma Ma Fem Ma Ma
Sex
no no yes no yes no no no yes yes yes yes
Hypertension
ICU patient status no no yes no yes yes no no yes yes no yes
Diabetes
21 27 17 16 19 18 38 35 14 21 3 4
Days in ICU
no yes yes yes no yes yes yes yes yes no no
Secondary bacterial
no no yes no no no no yes no yes no no
Mortality
yes no yes no no yes yes yes no yes no no
Echocardiography
yes no yes no no no yes yes yes yes no no
Guidance D-Dimer > 10,000
tools no no no no no no yes no no no yes no
US/CT
yes yes yes yes yes yes yes yes yes yes yes yes
Troponin > 14 ng/l
yes no yes no no yes no yes no yes no no
Nitric Oxide
Improve yes yes yes no no yes yes yes no yes no no
Prone
yes yes yes yes yes yes yes yes yes yes no no
02 Invasive mechanical
yes yes no yes yes yes yes yes yes yes yes yes
delivery Fluids
Interventi
no yes yes yes yes yes yes yes yes yes no no
Vasopressor therapy
on
yes no no no no no no no no no no no
ECMO
yes yes yes yes yes yes yes yes yes yes yes yes
Enoxaparin
yes yes no yes no yes yes yes yes yes no no
Heparin
no no yes no no no no no no no no no
TPA
yes yes yes yes yes yes yes yes yes yes yes yes
Ferritin > 500
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no yes yes no no no yes yes yes yes yes yes
Guidance CRP > 90 mg/l*
Control
no yes no yes yes no no yes yes yes yes yes
tools IP-10 > 1,000
inflamma
no yes yes yes yes yes yes yes yes yes no yes
Interventi Tocilizumab
tion
yes yes yes yes yes yes yes yes yes yes yes yes
on Corticosteroids
yes yes no no yes yes yes yes yes no yes no
Guidance PCR (viral load)**
no yes yes yes no no no yes no yes no yes
TRAIL level < 25
tools
no no yes no no yes yes no yes no no no
Zinc
Viral no yes no yes yes no no yes no yes yes no
Lopinavir/Ritonavir
no no no no no no no no no yes no no
clearance Interventi Remdesivir
yes yes yes yes yes yes yes yes yes yes yes yes
on Hydroxychloroquine
no yes yes yes yes yes yes yes yes yes yes yes
Azithromycin
no yes no no no no no yes no yes no yes
Convalescent Plasma
ICU, Intensive care unit; US, Ultrasound; CT, Computed tomography; ECMO, Extracorporeal membrane
oxygenation; TPA, Tissue plasminogen activator; PCR, polymerase chain reaction.
IP-10 levels indicate hyperinflammation and associate with mortality
A dynamic clinical decision support protocol that includes IP-10 was employed for
managing the SARS-CoV-2 patients admitted to the COVID-19 dedicated medical center (Figure
4). Based on an evolving understanding of the complex host-pathogen interaction during disease
progression, the goals of this protocol were to improve oxygen delivery and control inflammation
without compromising viral clearance.
To assess IP-10 levels in SARS-CoV-2 infection as compared to other viral infections and
healthy subjects, a retrospective analysis was performed that included cohorts from previous
studies.
The data shows that IP-10 levels are significantly higher in viral infections associated with
pulmonary pathology. IP-10 levels in the non-ICU (n = 40) and ICU (n = 12) COVID-19 patients
and IP-10 levels in healthy subjects (n = 98) and virally infected patients recruited in Curiosity14
(NCT01917461) and Observer (NCT03011515) studies (n = 182) with PCR-confirmed viral
infections including human rhinovirus (HRV), respiratory syncytial virus (RSV), influenza (Flu),
and human coronavirus (HCoV). For the COVID-19 patients, IP-10 was measured using MeMed
Key™ at multiple time points and the first IP-10 measurement is shown.
Focusing on the prospective cohort of 52 SARS-CoV-2 positive patients, IP-10 >1,000
pg/ml correlated with increased COVID-19 severity score5 (p<0.01) and ICU admission (p<0.05;
Table 17).
Table 17
01000 p-value
General
Age 68.8 (21.5) 68.5 (14.2) 0.388
Female 12 (36.0%) 4 (21.0%) 0.353
Smoking Status
Smoker 1 (3.0%) 2 (11.0%) 0.546
Past Smoker 2 (6.0%) 1 (5.0%) 1
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01000 p-value
Other
Death 2 (6.0%) 2 (11.0%) 0.617
Discharged 32 (97.0%) 19 (100.0%) 1
Highest Temp 37.2 (1.0) 38.1 (1.3) 0.001
Duration of Hospital Stay 9.0 (13.8) 12.0 (15.0) 0.052
Time between PCR Positive and BV Test 5.0 (6.0) 3.0 (5.0) 0.051
ICU 4 (12.0%) 8 (42.0%) 0.019
Ventilated 6 (18.0%) 6 (32.0%) 0.317
Time between Symptoms and BV Test 7.0 (12.0) 7.0 (8.0) 0.199
Time between Symptoms and ICU 8.0 (2.8) 8.5 (5.5) 0.244
Time between Symptoms and PCR Positive 2.0 (3.0) 4.0 (4.5) 0.255
Time on Ventilation (days) 0.0 (0.0) 0.0 (12.5) 0.174
Time between PCR Positive and ICU 4.0 (2.2) 4.5 (1.8) 0.265
Time between PCR Positive and PCR 18.0 (11.8) 25.0 (13.2) 0.112
Co-infection
Bacterial Infection 4 (12.0%) 6 (32.0%) 0.142
Co-infection 4 (12.0%) 6 (32.0%) 0.142
Fungal Infection 2 (6.0%) 2 (11.0%) 0.617
Conditions
Diabetes 13 (39.0%) 9 (47.0%) 0.771
Cardiovascular Disease 7 (21.0%) 2 (11.0%) 0.458
Cerebrovascular disease 3 (9.0%) 0 (0.0%) 0.291
Chronic Lung Disease 3 (9.0%) 4 (21.0%) 0.4
Chronic renal disease 4 (12.0%) 2 (11.0%) 1
Dyslipidemia 4 (12.0%) 5 (26.0%) 0.26
Hypertension 11 (33.0%) 13 (68.0%) 0.021
Hypothyroidism 2 (6.0%) 4 (21.0%) 0.175
Malignancy 1 (3.0%) 4 (21.0%) 0.054
Obesity (BMI>30 kg/m2) 7 (21.0%) 2 (11.0%) 0.458
Immunodeficiency 1 (3.0%) 0 (0.0%) 1
Symptoms
Sore throat 1 (3.0%) 0 (0.0%) 1
Vomiting 0 (0.0%) 2 (11.0%) 0.129
Urinary Complaints 1 (3.0%) 0 (0.0%) 1
Sputum production 1 (3.0%) 0 (0.0%) 1
Weakness 8 (24.0%) 4 (21.0%) 1
Rhinorrhea 1 (3.0%) 0 (0.0%) 1
Lethargy 2 (6.0%) 0 (0.0%) 0.527
Agitation 0 (0.0%) 1 (5.0%) 0.365
Chest pain 2 (6.0%) 0 (0.0%) 0.527
Confusion 2 (6.0%) 0 (0.0%) 0.527
Chills 1 (3.0%) 0 (0.0%) 1
Diarrhea 4 (12.0%) 2 (11.0%) 1
Dyspnea 12 (36.0%) 11 (58.0%) 0.157
Fatigue 1 (3.0%) 2 (11.0%) 0.546
Fever 16 (48.0%) 13 (68.0%) 0.247
Headache 4 (12.0%) 1 (5.0%) 0.641
Loss of Taste and Smell 4 (12.0%) 1 (5.0%) 0.641
Myalgia 4 (12.0%) 0 (0.0%) 0.284
Nausea 2 (6.0%) 1 (5.0%) 1
Cough 15 (45.0%) 11 (58.0%) 0.565
Markers
AST (GOT) (u/l) max 26.6 (35.2) 55.7 (37.9) 0.005
ALT (GPT) (u/l) max 28.6 (46.1) 63.4 (85.1) 0.017
Albumin (g/dl) max 3.8 (0.6) 3.7 (0.3) 0.082
INR max 1.1 (0.2) 1.1 (0.3) 0.343
Glucose (mg/dl) max 131.2 (110.8) 184.4 (150.3) 0.017
LDH (u/l) max 491.0 (312.8) 689.0 (266.0) 0.011
Lymph.abs (K/micl) max 1.3 (0.8) 1.3 (0.9) 0.402
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01000 p-value
Lymph.abs (K/micl) min 0.9 (0.7) 0.5 (0.4) 0.004
Neu.abs (K/micl) max 5.1 (3.8) 8.0 (8.5) 0.014
Neu.abs (K/micl) min 3.7 (2.5) 2.8 (1.5) 0.045
PLT (10^3) max 374.0 (202.0) 375.0 (158.5) 0.421
PLT (10^3) min 229.0 (129.0) 162.0 (55.0) 0.013
PaO2/FiO2 max 238.0 (84.0) 344.5 (136.5) 0.388
Total protein (g/dl) max 7.0 (0.7) 7.0 (0.7) 0.406
PaO2/FiO2 min 83.0 (63.0) 89.5 (46.5) 0.44
Troponin max 12.0 (33.0) 25.0 (40.0) 0.059
Urea max 34.6 (38.0) 58.0 (57.9) 0.002
Ferritin min 281.1 (338.8) 313.3 (365.0) 0.202
Ferritin max 449.4 (697.1) 901.0 (1147.6) 0.005
D-Dimer max 1173.5 (2575.2) 2175.0 (6061.5) 0.055
Creatinine (mg/dl) max 0.9 (0.4) 1.0 (0.4) 0.008
Bili total (mg/dl) max 0.4 (0.4) 0.8 (0.6) 0.018
CRP min (mg/l) 20.3 (54.2) 19.0 (52.2) 0.458
Median CRP (mg/l) 38.4 (70.4) 107.4 (88.8) 0
CRP max (mg/l) 66.1 (82.3) 169.5 (109.0) 0
Scores
COVID-19 Severity 11 (33.0%) 15 (79.0%) 0.003
qSOFA (on admission) 0.0 (1.0) 1.0 (1.0) 0.064
Treatments
Solumedrol 4 (12.0%) 8 (42.0%) 0.019
Hydrocortisone 5 (15.0%) 5 (26.0%) 0.467
Systemic Steroids (IV/PO) 10 (30.0%) 12 (63.0%) 0.04
Renal Replacement Therapy 1 (3.0%) 1 (5.0%) 1
Remdesivir 0 (0.0%) 1 (5.0%) 0.365
Prednisone 3 (9.0%) 3 (16.0%) 0.656
Nitric Oxide 2 (6.0%) 3 (16.0%) 0.342
ECMO 1 (3.0%) 0 (0.0%) 1
Vasopressors 5 (15.0%) 6 (32.0%) 0.181
Convalescent Plasma 1 (3.0%) 6 (32.0%) 0.007
Azithromycin 16 (48.0%) 19 (100.0%) >0.001
Antiviral Lopinavir/Ritonavir 0 (0.0%) 7 (37.0%) >0.001
Antiviral Hydroxychloroquine plus 14 (42.0%) 19 (100.0%) >0.001
Antiviral Hydroxychloroquine 17 (52.0%) 19 (100.0%) >0.001
Antibiotics 15 (45.0%) 15 (79.0%) 0.023
Tocilizumab 5 (15.0%) 8 (42.0%) 0.047
Days under Systemic Steroids 0.0 (3.0) 5.0 (10.5) 0.007
First BV test
TRAIL (pg/ml) 58.2 (22.5) 50.7 (52.7) 0.131
CRP (mg/l) 38.4 (70.4) 107.4 (88.8) >0.001
IP-10 (pg/ml) 333.2 (419.5) 1984.5 (1525.6) >0.001
BMI, Body mass index; AST, Aspartate transaminase; AL, Alanine transaminase; LDH, Lactate
dehydrogenase; Lymph. Abs, Absolute lymphocytes; Neu. Abs, Absolute neutrophils; PLT,
platelets; PaO2/FiO2, Ratio of arterial oxygen partial pressure to fractional inspired oxygen;
ECMO, Extracorporeal membrane oxygenation; qSOFA, quick sequential organ failure
assessment; TRAIL, TNF-related apoptosis inducing ligand; IP-10, interferon-γ induced protein
(also known as CXCL-10); CRP, C-reactive protein.
Among the 12 ICU patients, 11 were considered hyperinflammatory, and 10 received
tocilizumab and methylprednisolone; patient #18 was admitted to the ICU after pneumothorax
surgery and was not administered tocilizumab. For COVID-19 patients (n = 52) and the sub-group
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of ICU patients (n = 12), the number of days with IP-10 > 1,000 pg/ml was associated with
mortality (Tables 18 and 19).
Table 18
Death Days of IP-
>=1000pg/ml
1 - 3140- EY
0
FALSE
2 - 5011- IG
2
FALSE
4 - 5449- BTR
2
FALSE
- 5557- MP
1
FALSE
6 - 2726- SEBB
0
FALSE
7 - 4267- HH
0
FALSE
8 - 1493- MR
6
TRUE
9 - 5065- YK
3
FALSE
- 8752- YHT
FALSE
11 - 4108- SG
9
TRUE
12 - 3216- SS
0
FALSE
13 - 1434- HD
0
FALSE
14 - 5051- MM
1
FALSE
- 3050- BP
0
FALSE
16 - 1202- HT
0
FALSE
17 - 2000- MBG
0
FALSE
18 - 6494- NBT
1
FALSE
19 - 3091- RM
0
FALSE
- 5921- RA
1
FALSE
21 - 5239- YAF
3
FALSE
22 - 5397- SOC
0
FALSE
23 - 3092- MSA
0
FALSE
24 - 5388- GZM
0
FALSE
- 4604- STFR
0
FALSE
26 - 2964- ARH
0
FALSE
27 - 5304- NAG
1
FALSE
28 - 3422- SHY
3
FALSE
29 - 6044- ZAPY
0
FALSE
- 5488- DAZ
2
FALSE
31 - 5422- DSM
0
FALSE
32 - 3167- TAM
0
TRUE
33 - 5002- BAL
0
FALSE
34 - 7461- TS
0
FALSE
- 1159- LM
0
FALSE
36 - 2771- AS
0
FALSE
37 - 1434- BDN
0
FALSE
38 - 1075 - AMH
1
FALSE
39 - 5416- FY
0
FALSE
40 - 5587-GAR
7
FALSE
41 - 9601-RY
0
FALSE
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42 - 3692-AMA
1
FALSE
43 - 3185-LMH
0
FALSE
44 - 3426- AM
3
FALSE
45 - 4154- RA
2
FALSE
46 - 56824- YH
0
FALSE
47 - 3804- KM
0
FALSE
48 - 56825-SH
0
FALSE
49 - 5930-GMY
0
FALSE
50 - 5643-BHN
0
FALSE
51 - 3068-YS
0
FALSE
52 - 8222-TE
0
FALSE
Table 19
Death Days of IP-10>=1000pg/ml
1 - 3140- EY 0
FALSE
2 - 5011- IG 2
FALSE
4 - 5449- BTR 2
FALSE
- 5557- MP 1
FALSE
6 - 2726- SEBB 0
FALSE
7 - 4267- HH 0
FALSE
8 - 1493- MR 6
TRUE
9 - 5065- YK 3
FALSE
11 - 4108- SG 9
TRUE
18 - 6494- NBT 1
FALSE
38 - 1075 - AMH 1
FALSE
Tables 18 and 19 provide data that shows that mortality correlates with the number of days
IP-10 >1000 pg/ml in SARS-CoV-2 positive patients (n = 52 – Table 18) and among the subset of
ICU patients (n = 12, Table 19). IP-10 was measured using MeMed Key™ at multiple time points
during the SARS-CoV-2 positive patient’s hospitalization. At least one IP-10 measurement ≥
1,000 pg/ml in a given day was sufficient to classify as a day > 1,000 pg/ml.
In contrast, the number of days with CRP > 90 mg/l or ferritin > 500 mg/dl was not
associated with mortality (Table 20).
Table 20
Days of Days of Days of IL- Days of IL- Deat
CRP>=90mg/l Ferritin>=500ng/dl 6>=80pg/ml 6>=100pg/ml h
1 - 3140- FAL
EY 0 20 4 4 SE
11 - 4108- TRU
SG 4 6 8 8 E
18 - 6494- FAL
NBT 6 12 0 0 SE
2 - 5011- FAL
IG 1 30 8 8 SE
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3 - 4139- TRU
ST 2 17 8 8 E
38 - 1075 - FAL
AMH 2 2 2 2 SE
4 - 5449- FAL
BTR 0 18 2 2 SE
- 5557- FAL
MP 0 21 2 2 SE
6 - 2726- FAL
SEBB 0 1 2 2 SE
7 - 4267- FAL
HH 13 39 12 12 SE
8 - 1493- TRU
MR 5 6 10 10 E
9 - 5065- FAL
YK 4 19 15 15 SE
Overall, 3 ICU patients died; patient #3 died of candidemia. Focusing on the remaining 2
ICU patients who died, IP-10 > 1,000 pg/ml was exhibited for an average of 7.5 days (standard
deviation [SD], 2.1 days) in contrast to the 9 surviving patients who exhibited IP-10 > 1,000 pg/ml
for an average of 1 day (SD, 1.1 day; p=0.04).
The data in Table 20 shows that that mortality is not correlated with the number of days
CRP > 90 mg/l (upper panel) or number of days ferritin > 500 ng/dl (lower panel) in the ICU
patients (n = 12). In addition, the data in Table 20 shows that mortality is not correlated with the
number of days IL-6 > 80 pg/ml (upper panel) or number of days IL-6 > 100 pg/ml (lower panel)
in the ICU patients (n = 12)
CRP and ferritin were measured at multiple time points during the 12 patient’s stay in the
ICU. At least one CRP > 90 mg/l or ferritin > 500 ng/dl on a given day was sufficient for the
analysis. IL-6 was measured at multiple time points during the 12 patient’s stay in the ICU. At
least one IL-6 > 80 pg/ml (upper panel) or IL-6 > 100 pg/ml in a given day was sufficient for the
analysis.
All ICU patients exhibited reduced IP-10 levels within 3 days of their first corticosteroid
treatment administered in the ICU (Figure 5). In the case where patients were given additional
corticosteroids a decrease in IP-10 levels was observed (Figures 6A-C); patients #8 and #11 who
exhibited multiple days of highly elevated IP-10 died. Retrospective examination of IL-6 levels in
the ICU patients revealed a weak correlation with IP-10 levels (Pearson correlation coefficient,
0.26) and no association with mortality, both for number of days with IL-6 > 80 pg/ml or IL-6 >
100 pg/ml (Table 20).
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TRAIL levels flag viral persistence
TM
The MeMed Key platform was used to provide measurements of the immune protein
TRAIL. Applying the formal criterion of two negative SARS-CoV-2 PCR tests as confirmation of
viral clearance, eight out of the 12 ICU patients were clear of the virus within the study period.
TRAIL ≤ 25 pg/ml was associated with longer duration of SARS-CoV-2 positivity (p = 0.012).
The number of days TRAIL ≤ 25 pg/ml was found to correlate with the number of virus positive
days (Pearson correlation coefficient, 0.7).
EXAMPLE 6
A multinational clinical study for the purpose of developing a novel signature (comprising
TRAIL, IP-10 and CRP proteins) that aids in stratifying the likelihood of severe COVID-19
infection was set up. The cohort included SARS-CoV-2 positive adult patients, sampled within 7
days of emergency department (ED) arrival. No exclusion criteria were applied. A patient was
defined as severe if he/she met within 14 days from blood draw one or more of the following
severe outcomes: mortality or ICU-level care (i.e., ICU admission, high flow nasal cannula,
continuous positive airway pressure, bi-level airway pressure, invasive mechanical ventilation).
A total of 518 US, Israeli, and German SARS-CoV-2 positive patients were recruited
between March 20 and Nov 20, of which 124 were excluded due to unavailability of serum (n=48),
age <18 years (n=5), sampling >7 days from ED arrival (n=68) and missing data (n=3). The
remaining 394 patients were eligible for inclusion in the derivation cohort, 281 of which were
severe and 113 non-severe (Figure 7).
Table 21 shows the demographics of the derivation cohort across severe and non-severe
patients. Severe patients exhibited a significantly longer length of stay (Median; 11 vs 5 days; p <
0.01), were more likely to be male (%; 70.8 vs 54.4; p < 0.01) and older (Median; 65 vs 60 years;
p < 0.01), in line with previous reports. Significance of difference between proportions was
calculated using Fisher's exact test. Significance of difference in numeric variables was calculated
using the Mann–Whitney U test.
Table 21
Statistic Severe Non-Severe Derivation
s (n=113) (n=281) cohort (n=394)
Age (years) Median 65.0
60.0 (27.0) 61.3 (25.8)
(IQR) (21.5)
Male sex 80
n (%) 153 (54.4%) 233 (59.1%)
(70.8%)
Time from ED arrival to blood draw (days) Median
2.0 (2.0) 1.0 (2.0) 1.0 (2.0)
(IQR)
Severe outcome
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ICU admission 51
n (%) 0 (0.0%) 51 (12.9%)
(45.1%)
High flow nasal cannula, bilevel airway pressure, or 65
n (%) 0 (0.0%) 65 (16.5%)
continuous positive airway pressure (57.5%)
Invasive mechanical ventilation 49
n (%) 0 (0.0%) 49 (12.4%)
(43.4%)
Mortality 30
n (%) 0 (0.0%) 30 (7.6%)
(26.5%)
Length of hospital stay (days) * Median 11.0
.0 (8.0) 6.5 (10.0)
(IQR) (13.8)
Coexisting conditions
Hypertension 47
n (%) 100 (35.6%) 147 (37.3%)
(41.6%)
Diabetes 36
n (%) 68 (24.2%) 104 (26.4%)
(31.9%)
Chronic heart failure
n (%) 6 (5.3%) 10 (3.6%) 16 (4.1%)
Active malignancy
n (%) 8 (7.1%) 7 (2.5%) 15 (3.8%)
RESULTS
The levels of CRP and IP-10 were found to be significantly higher in patients with severe
as compared to non-severe COVID-19 infection: median (and interquartile range) levels of CRP
were 167 (178) mg/L vs. 42 (84) mg/L, (p<0.01); and IP-10 levels were 1632 (2250) pg/ml vs.
420 (681) pg/ml, (p<0.01). TRAIL exhibits the opposite pattern, with concentration significantly
lower in patients with a severe infection as compared to a non-severe infection: 31 (18) pg/ml vs.
61 (51) pg/ml, (p<0.01).
To integrate the protein levels into a single score the present inventors examined multiple
computational models. Among the several top performing models that differentiated between
severe and non-severe COVID-19 infection, they chose regularized logistic regression y. Only
original features without any polynomial combination were selected in order to reduce the chance
of overfitting. The features’ discriminatory potential was evaluated by 10-fold cross-validation
yielding an AUC of 0.86 (95% CI, 0.81 – 0.9). Adding age or sex to the host-protein features, did
not improve results. The final COVID-19 Severity model was trained on the entire derivation
cohort and showed comparable results to the 10-fold cross validation indicating that the model was
not overfitted.
A pragmatic assessment of performance that helps to translate a risk stratification model
into a clinically intuitive and easy-to-use tool is to assign patients into bins with increasing
signature scores and evaluate if there is a meaningful increase in the relative likelihood of severe
outcome across the bins. To that end the present inventors assigned each patient to a specific bin
based on their COVID-19 Severit score, and within the bin according to their severity outcome.
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99
As can be seen in Table 22, the relative likelihood of COVID-19 severe outcome significantly
increases across the four bins.
Table 22. Score Distribution Across Bins in the Derivation Cohort
Score bins %Patients n total n n % Severe % NonSevere *LR (95% CI)
Severe NonSevere
80 < score ≤ 100 19.0 75 54 21 72.0 28.0 6.4 (4.1-10.1)
40 ≤ score ≤ 80 29.4 116 46 70 39.7 60.3 1.6 (1.2-2.2)
< score < 40 20.1 79 10 69 12.7 87.3 0.4 (0.2-0.7)
0 ≤ score ≤ 20 31.5 124 3 121 2.4 97.6 0.1 (0.0-0.2)
Total 100.0 394 113 281
*The Likelihood Ratio (LR) for a score bin was calculated as the fraction of severe patients in the
bin out of all severe patients, divided by the fraction of non-severe patients in the bin out of all
non-severe patients.
The Cochran–Armitage exact statistical test was used to calculate the significance of the trend of
severe and non-severe patient distribution across the score bins, resulting in p-value < 0.01.
In addition, the present inventors assessed the probability for 14-day mortality with a
Kaplan-Meyer survival estimator across the COVID-19 Severity score bins (Figure 8A). In total
patients died. Survival distribution was significantly different comparing bins 1-2 to bins 3-4
(Figure 8B; p-value < 0.01).
Several parameters (e.g., age) and biomarkers (e.g., IL-6) have been reported as candidate
tools for aiding the clinician in discriminating between severe and non-severe COVID-19
infection. The present inventors compared the performance of the COVID-19 Severity™
algorithm to that of various biomarkers and parameters using AUROC analysis. In the analysis
presented in Figure 9, the AUROC was calculated for a given parameter or biomarker based on
the subset of patients with the available measurement. Additionally, a head-to-head comparison is
presented in Table 23, where AUROC was calculated for all biomarkers based only on the subset
of patients that had measurements for both comparator biomarker and the COVID-19 Severity
algorithm at time of blood draw. COVID-19 Severity was found to compare favorably to the
parameter and biomarkers tested.
Table 23 Performance of COVID-19 Severity signature Compared to Other Biomarkers
Comparator COVID-19 Severity™ p-value*
AUROC (95% CI) AUROC (95% CI) in
matching population
Age 0.58 (0.52, 0.65) 0.86 (0.81, 0.91) <0.01
IL6 0.77 (0.67, 0.87) 0.89 (0.81, 0.97) 0.03
PCT 0.77 (0.66, 0.88) 0.90 (0.82, 0.98) 0.04
CRP 0.78 (0.73, 0.83) 0.86 (0.81, 0.91) <0.01
Ferritin 0.65 (0.52, 0.78) 0.80 (0.69, 0.91) 0.05
Lactate 0.52 (0.31, 0.74) 0.84 (0.68, 1.00) 0.01
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100
EXAMPLE 7
Particular RNA pairs that can be used to rule in a viral infection of asymptomatic patients
are summarized in Table 24 herein below. The genes were selected based on their ability to
separate day 0-1 viral patients (time from symptoms) from non-infectious patients or were detected
early in a viral challenge study.
Table 24
Gene 1 Gene 2 ROC Sensitivity Specificity Fold change
AUC (absolute
value)
IFI27 IFI44L 1.00 1.00 1.00 183.8
IFI44L RSAD2 1.00 1.00 1.00 183.8
IFI44L SERPING1 1.00 1.00 1.00 94.4
IFI44 IFI44L 1.00 1.00 1.00 90.0
IFI44L IFIT1 1.00 1.00 1.00 87.0
IFI44L OAS3 1.00 1.00 1.00 86.0
HERC5 IFI44L 1.00 1.00 1.00 60.9
IFI44L USP18 1.00 1.00 1.00 53.1
CMPK2 IFI44L 1.00 1.00 1.00 47.6
CARD17 IFI44L 1.00 1.00 1.00 38.9
IFI44L PI3 1.00 1.00 1.00 36.5
EPSTI1 IFI44L 1.00 1.00 1.00 35.6
IFI44L MX1 1.00 1.00 1.00 32.7
IFI44L OAS2 1.00 1.00 1.00 32.1
IFI44L IFI6 1.00 1.00 1.00 29.3
IFI44L OASL 1.00 1.00 1.00 26.8
DDX60 IFI44L 1.00 1.00 1.00 25.4
IFI44L OAS1 1.00 1.00 1.00 24.7
IFI44L IFIT3 1.00 1.00 1.00 24.1
IFI44L LY6E 1.00 1.00 1.00 20.3
IFI44L IFIH1 1.00 1.00 1.00 16.5
IFI44L PLSCR1 1.00 1.00 1.00 16.4
IFI35 IFI44L 1.00 1.00 1.00 16.2
IFI44L LAP3 1.00 1.00 1.00 15.6
IFI44L IFIT2 1.00 1.00 1.00 15.0
IFI44L XAF1 1.00 1.00 1.00 14.6
ANKRD22 IFI44L 1.00 1.00 1.00 12.4
IFI44L RTP4 1.00 1.00 1.00 12.2
IFI44L IFIT5 1.00 1.00 1.00 11.9
IFI44L SAMD9L 1.00 1.00 1.00 11.2
IFI44L PARP12 1.00 1.00 1.00 11.1
GBP1 IFI44L 1.00 1.00 1.00 11.1
IFI44L TNFAIP6 1.00 1.00 1.00 10.9
AIM2 IFI44L 1.00 1.00 1.00 10.8
IFI44L PHOSPHO1 1.00 1.00 1.00 10.6
EIF2AK2 IFI44L 1.00 1.00 1.00 10.3
IFI44L ZBP1 1.00 1.00 1.00 9.6
ETV7 IFI44L 1.00 1.00 1.00 9.6
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DDX58 IFI44L 1.00 1.00 1.00 8.8
IFI44L STAT2 1.00 1.00 1.00 8.7
FAM101B IFI44L 1.00 1.00 1.00 8.4
GBP1P1 IFI44L 1.00 1.00 1.00 8.3
IFI44L ZCCHC2 1.00 1.00 1.00 8.2
IFI44L PARP14 1.00 1.00 1.00 7.3
DDX60L IFI44L 1.00 1.00 1.00 7.3
IFI44L SAMD9 1.00 1.00 1.00 6.8
IFI44L PARP9 1.00 1.00 1.00 6.7
IFI44L ISG15 1.00 1.00 1.00 6.1
CASS4 IFI44L 1.00 1.00 1.00 6.0
EMR3 IFI44L 1.00 1.00 1.00 5.9
IFI44L TNFSF10 1.00 1.00 1.00 3.3
IFI44L JUP 1.00 1.00 1.00 2.8
IFI27 JUP 0.93 0.86 1.00 2.8
IFI44L OTOF 1.00 1.00 1.00 1.9
IFI44L LAX1 1.00 1.00 1.00 1.8
IFI27 LAX1 0.99 0.93 1.00 1.8
JUP LAX1 0.89 0.93 0.77 1.8
EVI2A IFI44L 1.00 1.00 1.00 1.1
Particular RNA triplets that can be used to rule in a viral infection of asymptomatic patients
are summarized in Table 25, herein below. The genes were selected based on their ability to
separate day 0-1 viral patients (time from symptoms) from non-infectious patients or were detected
early in a viral challenge study.
Table 25
Gene 1 Gene 2 Gene 3 ROC Sensitivity Specificity Fold change
AUC (absolute value)
CARD1 IFI44L PI3 1.00 1.00 1.00 36.5
7
CMPK2 IFI44L PI3 1.00 1.00 1.00 36.5
HERC5 IFI44L PI3 1.00 1.00 1.00 36.5
IFI27 IFI44L PI3 1.00 1.00 1.00 36.5
IFI44 IFI44L PI3 1.00 1.00 1.00 36.5
IFI44L IFIT1 PI3 1.00 1.00 1.00 36.5
IFI44L OAS3 PI3 1.00 1.00 1.00 36.5
IFI44L PI3 RSAD2 1.00 1.00 1.00 36.5
IFI44L PI3 SERPIN 1.00 1.00 1.00 36.5
G1
IFI44L PI3 USP18 1.00 1.00 1.00 36.5
EPSTI1 IFI44L PI3 1.00 1.00 1.00 35.6
IFI44L MX1 PI3 1.00 1.00 1.00 32.7
IFI44L OAS2 PI3 1.00 1.00 1.00 32.1
IFI44L IFI6 PI3 1.00 1.00 1.00 29.3
IFI44L OASL PI3 1.00 1.00 1.00 26.8
DDX60 IFI44L PI3 1.00 1.00 1.00 25.4
IFI44L IFIT3 PI3 1.00 1.00 1.00 24.1
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IFI44L LY6E PI3 1.00 1.00 1.00 20.3
IFI44L IFIT2 PI3 1.00 1.00 1.00 15.0
IFI44L PI3 XAF1 1.00 1.00 1.00 14.6
IFI44L IFIT5 PI3 1.00 1.00 1.00 11.9
IFI44L PI3 TNFAIP 1.00 1.00 1.00 10.9
6
IFI44L PHOSPH PI3 1.00 1.00 1.00 10.6
O1
DDX58 IFI44L PI3 1.00 1.00 1.00 8.8
FAM10 IFI44L PI3 1.00 1.00 1.00 8.4
1B
IFI44L ISG15 PI3 1.00 1.00 1.00 6.1
CASS4 IFI44L PI3 1.00 1.00 1.00 6.0
EMR3 IFI44L PI3 1.00 1.00 1.00 5.9
IFI44L PI3 TNFSF1 1.00 1.00 1.00 3.3
0
IFI44L JUP PI3 1.00 1.00 1.00 2.8
IFI44L OTOF PI3 1.00 1.00 1.00 1.9
IFI44L LAX1 PI3 1.00 1.00 1.00 1.8
IFI27 JUP LAX1 0.99 0.93 1.00 1.8
EVI2A IFI44L PI3 1.00 1.00 1.00 1.1
All publications, patents and patent applications mentioned in this specification are herein
incorporated in their entirety by reference into the specification, to the same extent as if each
individual publication, patent or patent application was specifically and individually indicated to
be incorporated herein by reference. In addition, citation or identification of any reference in this
application shall not be construed as an admission that such reference is available as prior art to
the present invention. To the extent that section headings are used, they should not be construed
as necessarily limiting. In addition, any priority documents of this application are hereby
incorporated herein by reference in their entirety.
Claims (49)
- CLAIMED IS: 1. A method of classifying the severity of a coronaviral infection of a subject, comprising measuring the TRAIL and/or IP10 protein level in a body liquid sample of the subject, wherein said level is indicative of the severity of the coronaviral infection.
- 2. The method of claim 1, wherein said subject does not present with symptoms of an infectious disease.
- 3. The method of claim 1, wherein said subject presents with symptoms of an infectious disease.
- 4. The method of claim 3, wherein said symptoms comprise fever.
- 5. The method of claim 1, further comprising measuring the level of at least one determinant set forth in Table 1.
- 6. The method of claim 5, wherein said at least one determinant is selected from the group consisting of PCT, IL-6 and CRP.
- 7. The method of claim 1, further comprising measuring the level of at least one determinant selected from the group consisting of ferritin, d-dimer and lactate.
- 8. The method of any one of claims 1-7, further comprising measuring the level of CRP.
- 9. The method of claim 1, further comprising treating the subject who has been classified as severe with a management selected from the group consisting of oxygen therapy, non- invasive ventilation, mechanical ventilation, invasive monitoring, last-resort drug, sedation, intensive care admission, surgical intervention, hospital admittance, anti-viral drug, anti-viral regimen, anti-bacterial drug, anti-fungal drug, immune-globulin treatment isolation, glucocorticoid therapy, extracorporeal membrane oxygenation and kidney replacement therapy.
- 10. The method of claim 1, wherein the coronavirus of said coronaviral infection is severe acute respiratory syndrome coronavirus (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV).
- 11. A method of managing treatment of a disease which brings about a dysregulation of an immune and/or inflammatory response of a subject, said subject already being medicated with a first treatment to treat said disease comprising: (a) measuring an amount of IP10 and/or TRAIL in a sample of the subject; and (b) selecting the treatment or treatment course based on whether the level of IP10 is above a predetermined threshold and/or whether the level of TRAIL is below a predetermined threshold in said sample.
- 12. The method of claim 11, wherein said disease is a viral disease.
- 13. The method of claim 12, wherein said viral disease is a respiratory viral disease.
- 14. The method of claim 13, wherein said respiratory viral disease is COVID-19.
- 15. The method of claim 13, wherein said respiratory viral disease comprises acute respiratory distress syndrome (ARDS).
- 16. The method of claim 13, wherein the virus of said respiratory viral disease is selected from the group consisting of RSV, Flu A, Flu B, HCoV and SARS-Cov-2.
- 17. The method of claim 11, wherein said predetermined threshold for IP10 is above 1000 pg/ml.
- 18. The method of claim 17, further comprising determining the length of time for which the level of IP10 is above said predetermined threshold, wherein said length of time is indicative that the treatment or treatment course should be altered.
- 19. The method of claim 11, wherein said predetermined threshold for TRAIL is below 25 pg/ml.
- 20. The method of claim 19, further comprising determining the length of time for which the level of TRAIL is below a predetermined threshold, wherein said length of time is indicative that the treatment or treatment course should be altered.
- 21. The method of claim 11, wherein said subject is hospitalized.
- 22. The method of claim 11, wherein said subject is non-hospitalized.
- 23. The method of claim 11, wherein said subject is in intensive care.
- 24. The method of claim 11, wherein said first treatment comprises a therapeutic agent.
- 25. The method of claim 11, wherein said first therapeutic agent comprises a steroid agent.
- 26. The method of claim 24, wherein said therapeutic agent comprises an agent which reduces the activity or amount of IL-6.
- 27. The method of claim 24, wherein said altering the treatment comprises altering the dose of said therapeutic agent based on the level of IP10 and/or TRAIL.
- 28. The method of claim 11, wherein step (b) is effected no more than 12 hours following step (a).
- 29. The method of claim 11, wherein altering the treatment comprises terminating said first treatment.
- 30. The method of claim 11, wherein altering the treatment comprises initiating a second treatment.
- 31. A method of predicting onset of a cytokine storm in a subject having a disease which brings about a dysregulation of an immune and/or inflammatory response, the method comprising: (a) measuring an amount of IP10 in a sample of the subject; and (b) predicting a cytokine storm based on the level of IP10.
- 32. A method of distinguishing between a system shutdown diagnosis and a cytokine storm diagnosis in a subject with a viral disease, comprising measuring the expression level of TRAIL in a sample of the subject, wherein when said level of TRAIL is above a predetermined level, a cytokine storm diagnosis is ruled in and/or when said level of TRAIL is below a predetermined level, a system shutdown diagnosis is ruled in.
- 33. The method of claims 31 or 32, further comprising analyzing the level of at least one additional determinant set forth in Tables 1 or 2.
- 34. The method of claim 32, wherein the amount of IP10 above 1000 pg/ml is indicative of onset of cytokine storm.
- 35. The method of claims 31 or 34, wherein the predicting comprises an estimation f the time of onset.
- 36. The method of claims 32 or 34, wherein said cytokine storm is the result of an immune-based therapy selected from the group consisting of Tumor-Infiltrating Lymphocyte (TIL) Therapy, Engineered T Cell Receptor (TCR) Therapy, Chimeric Antigen Receptor (CAR) T Cell Therapy and Natural Killer (NK) Cell Therapy.
- 37. The method of claim 32, wherein said disease is selected from the group consisting of infectious disease, Ulcerative colitis, Crohn’s disease, Rheumatoid arthritis, Sjogren syndrome, Kawasaki disease, Guillain-Barre syndrome, Lupus, Asthma, COPD, Multiple sclerosis, vasculitis and Psoriasis.
- 38. The method of claim 37, wherein said infectious disease is a viral disease.
- 39. A method of diagnosing the severity of a viral disease of a subject, comprising measuring the expression level of a first determinant set forth in Table 1 and the level of a clinical measurement set forth in Table 2 in a sample of the subject, wherein said levels are indicative of the severity of the viral disease.
- 40. The method of claim 39, wherein a virus of said viral infection is selected from the group consisting of Adenovirus, Bocavirus, Coronavirus, Enterovirus, Influenza virus, Metapneumovirus, Parainfluenza virus, Respiratory syncytial virus and Rhinovirus.
- 41. The method of claims 32, 38 or 39, wherein said viral disease is a respiratory viral disease.
- 42. The method of claim 32, 38 or 39 wherein said viral disease is transmitted via an influenza virus.
- 43. The method of claim 32, 38 or 39, wherein said viral disease is transmitted via a coronavirus.
- 44. The method of claim 43, wherein said coronavirus is selected from the group consisting of severe acute respiratory syndrome coronavirus (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV).
- 45. The method of any one of claims 31-44, further comprising measuring the amount of at least one additional determinant selected from the group consisting of IL-6, PCT and CRP.
- 46. A method of analyzing the contagiousness of a non-symptomatic subject suffering from an infection, comprising measuring the level of a determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of the subject, wherein: (i) when said level of TRAIL is higher than the level of TRAIL in a subject who does not have an infection, it is indicative that the non-symptomatic subject is contagious; and/or (ii) when said level of IP10 is higher than the level of IP-10 in a subject who does not have an infection, it is indicative that the non-symptomatic subject suffering from an infection is contagious.
- 47. A method of analyzing the degree of contagiousness of a test subject suffering from an infection comprising measuring the level of a determinant selected from the group consisting of TRAIL and IP10 in a body liquid sample of the subject, wherein the level of TRAIL and/or IP10 is proportional to the degree of contagiousness of the test subject suffering from the infection.
- 48. The method of claims 46 or 47, further comprising recommending isolation of said subject upon diagnosis as being contagious.
- 49. The method of claim 46, wherein: (i) when said level of TRAIL is at least 10 % higher than the level of TRAIL in a subject who does not have an infection, it is indicative that the non-symptomatic subject is contagious; and/or (ii) when said level of IP10 is at least 25 % higher than the level of TRAIL in a subject who does not have an infection, it is indicative that the non-symptomatic subject is contagious. Dr. Hadassa Waterman Patent Attorney G.E. Ehrlich (1995) Ltd. 35 HaMasger Street Sky Tower, 13th Floor Tel Aviv 6721407
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